OPTIMIZATION OF MEDIUM COMPONENTS AND PROCESS

PARAMETERS FOR ENHANCED PRODUCTION OF

LACTASE BY A BACTERIUM ISOLATED FROM DAIRY

EFFLUENT

A THESIS

submitted by

Mr. T.C.Venkateswarulu

for the award of the degree

of

DOCTOR OF PHILOSOPHY Under the guidance of

Dr. Vidya Prabhakar Kodali Dr. Bharath Kumar Ravuru

Department of Biotechnology Department of Biotechnology

VS University, Nellore VFSTR University, Vadlamudi

DEPARTMENT OF BIOTECHNOLOGY

VIGNAN’S FOUNDATION FOR SCIENCE, TECHNOLOGY AND RESEARCH

UNIVERSITY, VADLAMUDI

GUNTUR – 522 213 ANDHRA PRADESH, INDIA

JUNE 2017

ii

iii

DECLARATION

I certify that

a. The work contained in the thesis is original and has been done by myself under

the general supervision of my supervisor.

b. I have followed the guidelines provided by the Institute in writing the thesis.

c. I have conformed to the norms and guidelines given in the Ethical Code of

Conduct of the Institute.

d. Whenever I have used materials (data, theoretical analysis, and text) from other

sources, I have given due credit to them by citing them in the text of the thesis and

giving their details in the references.

e. Whenever I have quoted written materials from other sources, I have put them

under quotation marks and given due credit to the sources by citing them and

giving required details in the references.

f. The thesis has been subjected to plagiarism check using a professional software

and found to be within the limits specified by the University.

g. The work has not been submitted to any other Institute for any degree or diploma.

(Mr. T.C.Venkateswarulu)

iv

v

THESIS CERTIFICATE

This is to certify that the thesis entitled "OPTIMIZATION OF MEDIUM

COMPONENTS AND PROCESS PARAMETERS FOR ENHANCED

PRODUCTION OF LACTASE BY A BACTERIUM ISOLATED FROM DAIRY

EFFLUENT" submitted by T.C.VENKATESWARULU to the Vignan’s Foundation

for Science, Technology and Research University, Vadlamudi, Guntur for the award of

the degree of Doctor of Philosophy is a bonafide record of the research work done by

him under our supervision. The contents of this thesis, in full or in parts, have not been

submitted to any other Institute or University for the award of any degree or diploma.

Dr. Vidya Prabhakar Kodali

Research Guide

Assistant Professor,

Department of Biotechnology

Vikrama Simhapuri University Place: Guntur

Nellore, Andhra Pradesh, India Date: 04 June 2017

Dr. Bharath Kumar Ravuru

Research co- Guide

Associate Professor,

Department of Biotechnology Place: Guntur

VFSTR University, Andhra Pradesh, India Date: 04 June 2017

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ACKNOWLEDGEMENTS

My time as a Ph.D. student has been a special period of my life to remember, thanks to

many people around me who have inspired and encouraged me to conquer this long

journey. Firstly I would like to thank my university, Vignan's Foundation for Science,

Technology and Research University and my supervisors, Dr. Vidya Prabhakar Kodali

and Dr. R. Bharath Kumar for their patience, guidance, encouragement and trust over the

course of my Ph.D. program. I am indebted to my guide, Dr. Vidya prabhakar Kodali, for

accepting me as his Ph.D. student. The caring nature makes him the most wonderful

person as well as guide that any scholar wishes in their Ph.D. period. I never forget his

balancing behavior of guiding me scientifically, teaching me ethically and caring me

more friendlily. I express my sincere thanks to Prof. Ramamoorthy, Rector and incharge

Vice Chancellor. I would like to especially thank my present HOD, Dr. D. Vijaya Ramu

and former HOD, Prof. S. Krupanidhi who played a significant role in the success of my

Ph.D. A very special thanks goes to Dr. N. Prakash Narayana Reddy, Post Doctoral

Fellow and E. Rajeswara Reddy, Asst. Professor, Department of Biotechnology and Ms.

K. Rohini, Post graduate student of Vignan's university for their never ending support and

extending a ready helping hand throughout my Ph.D.

I take this opportunity to thank, my colleagues Mr. D. John Babu, Mrs. M. Indira, Mr. A.

Ranganadha Reddy, Dr. N.S. Sampath, Dr. S. Asha, Prof. R. Venkatanadh, Mr. A.

Venkatanarayana, Dr. M.S Shiva Kiran, Dept. of Biotechnology. I would also like to

convey my sincere thanks to Dr. P. Vidhu Kampurath., Dean R&D and Dr. D. Vinay

Kumar, member doctoral committee, for giving me valuable feedbacks and keeping my

research in check.

I also would like to thank the non-teaching staff- Mr. Ramesh Babu, Mr. Nageswara rao,

Mr. Srinivas, Mrs. Renuka Devi and Students – L. Lakshmi and Leela Prasad.

I acknowledge VFSTR University, Vadlamudi, for providing the chemicals and lab

facilities to carry out the work and my sincere thanks to Vignan’s University, Guntur and

Vikrama Simhapuri University, Nellore, India for providing facilities.

Mr. T.C.Venkateswarulu

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ABSTRACT

OPTIMIZATION OF MEDIUM COMPONENTS AND PROCESS

PARAMETERS FOR ENHANCED PRODUCTION OF LACTASE BY

A BACTERIUM ISOLATED FROM DAIRY EFFLUENT

Lactose intolerance is characterized by indigestion of milk sugar called as lactose.

Indigestion of milk and milk based products in lactose intolerants can be treated with

supplementing lactase enzyme. Bacteria that produce lactase have been used to treat the

lactose intolerants. Commercially, this enzyme production from bacterial sources was not

reported for higher production. In the present study, dairy effluent was screened for

lactase producing bacteria by biochemical methods and found 46 lactase positive isolates.

The highest lactase yielding isolate was named as VUVD001 and this isolate was

characterized by morphological, biochemical and molecular methods. The VUVD001

isolate was stable at extreme pH and temperature conditions. The o-nitrophenyl-β-D-

galactopyranoside (ONPG) disc analysis was performed for the confirmation of lactase

producing activity. The produced lactase was partially purified followed by zymogram

analysis for confirmation of extracellular activity. The isolate VUVD001 was identified

as Bacillus subtilis by 16S rRNA gene sequencing and this strain was able to survive in a

temperature range of 20-55 oC, pH range of 5-8 and a salt concentration upto 8% of NaCl.

In addition, the production of lactase was improved by designing new medium through

optimization of nutrient components by one-factor-at-a-time (OFAT) and statistical

methods. Lactose and yeast extract were selected as preferable carbon and nitrogen

sources for lactase production. Further, addition of tryptophan and MgSO4 showed

enhanced lactase production. Statistical analysis proved to be a powerful tool in exploring

optimum fermentation conditions. The individual and interactive role of lactose, yeast

extract, magnesium sulfate and tryptophan concentration on lactase production was

examined by central composite design. The submerged fermentation with B. subtilis

VUVD001 strain produced an enhanced lactase activity of 63.54 U/ml in optimized

medium. The activity was improved 2.5 folds compared to classical optimization of

medium components. This result of the study confirmed that the designed medium was

x

useful to produce higher yield of lactase. Similarly, the optimal physical conditions were

determined in batch fermentation process using OFAT approach for the parsimonious

yield of lactase. The influence of physical conditions pH, temperature, incubation time

and inoculum size on enzyme production was also studied. The maximum activity of

lactase in shake flask culture was found to be 15.27 U/ml at optimized conditions of

incubation period 36 h, Temperature 37 oC, pH 7.0 and inoculums size 5%. Further, the

modeling and optimization were performed to enhance the production of lactase through

submerged fermentation by B. subtilis VUVD001 using artificial neural networks (ANN)

and response surface methodology (RSM). The effect of process parameters namely

temperature (oC), pH and incubation time (h) and their combinational interactions on

production was studied in shake flask culture by Box-Behnken design. The model was

validated by conducting an experiment at optimized process variables which gave the

maximum lactase activity of 91.32 U/ml. The production of lactase 6 folds improved after

RSM optimization. This study clearly showed that both RSM and ANN models provided

desired predictions. However, the ANN model (R2=0.99456) gave a better prediction

when compared with RSM (R2=0.9496) for production of lactase. The findings of the

present study revealed that the B. subtilis VUVD001 strain could be used as a potential

strain for commercial production of lactase.

KEYWORDS: Response surface methodology, Artificial neural networks, Lactase,

B. subtilis VUVD001 strain

xi

CONTENTS

TITLE PAGE i

DECLARATION iii

CERTIFICATE v

ACKNOWLEDGEMENTS vii

ABSTRACT ix

CONTENTS xi

LIST OF TABLES xvii

LIST OF FIGURES xix

LIST OF SYMBOLS AND ABBREVIATIONS xxi

CHAPTER-I INTRODUCTION

1 INTRODUCTION 01

1.1 Lactose and Lactose intolerance 01

1.2 Types of intolerance 03

1.3 Milk allergy 03

1.4 Diagnosis 04

1.5 Solution to lactose intolerance with probiotics 04

1.6 Microbial enzymes 05

1.7 Global market for enzymes 06

1.8 Lactase 06

1.9 Structural properties of lactase 07

1.10 Applications of lactase 08

1.11 Thesis objectives 09

1.12 Thesis organizations 09

CHAPTER -II REVIEW OF LITERATURE

2.1 Microbial sources of lactases 11

2.1.1 Bacterial sources 11

2.1.2 Fungal sources 12

2.1.3 Yeasts sources 13

2.1.4 Plant and Animal sources 13

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2.2 Optimization of nutritional components and physical factors for

production of lactase 14

2.2.1 Medium components effect on the lactase production 14

2.2.2 Physical parameters effect on production of lactase 15

2.3 Statistical tools for improved lactase production from various 17

microbial sources 17

2.3.1 Response surface methodology (RSM) 17

2.3.2 Artificial neural network (ANN) 18

2.4 Motivation 19

CHAPTER-III BIOCHEMICAL AND MOLECULLAR CHARECTERIZATION

OF LACTASE PRODUCING BACTERIUM

3.1 INTRODUCTION 21

3.2 MATERIALS AND METHODS 22

3.2.1 Morphological characterization of lactase producing isolate 22

3.2.2 Growth curve 22

3.2.3 Antimicrobial Activity by Agar Diffusion Method 22

3.3 Biochemical characterization 22

3.3.1 X-gal plate method 22

3.3.2 Lactase enzyme assay 23

3.3.3 ONPG disc assay 23

3.3.4 Zymogram Analysis for Lactase Activity 23

3.3.5 IMViC, Sugar fermentation and enzyme tests 23

3.3.6 Salt tolerance test 23

3.3.7 Thermo stability test 24

3.4 Molecular characterization of the lactase producing isolate 24

3.4.1 Bacterial DNA isolation 24

3.4.2 PCR amplification 24

3.4.3 DNA sequencing 25

3.5 RESULTS AND DISCUSSION 25

3.5.1 Morphological characterization 25

3.5.1.1 Gram staining 25

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3.5.1.2 Spore staining 25

3.5.1.3 Growth curve 26

3.5.1.4 Antimicrobial activity 27

3.6 Biochemical characterization 27

3.6.1 X-gal plate method 27

3.6.2 Lactase enzyme assay for blue bacterial isolates 28

3.6.3 Qualitative assay for VUVD 001 by ONPG disc method 29

3.6.4 Zymogram Analysis 30

3.6.5 IMViC test 31

3.6.6 Carbohydrate utilization test 31

3.6.7 Starch hydrolysis and catalase test 32

3.6.8 Salt tolerance test 33

3.6.9 Thermo stability test 33

3.7 Molecular Characterization and identification of VUVD 001 36

3.7.1 Amplification 36

3.7.2 Multiple sequence alignment (MSA) 36

3.7.3 Cluster analysis 44

3.7.4 Phylogenetic tree construction 44

3.8 SUMMARY 46

CHAPTER-IV OPTIMIZATION OF NUTRITIONAL COMPONENTS OF THE

MEDIUM FOR THE ENHANCED LACTASE PRODUCTION

4.1 INTRODUCTION 47

4.2 MATERIALS AND METHODS 48

4.2.1 Microorganism and Shake flask fermentation 48

4.2.2 Effect of various nutrients sources 48

4.2.3 RSM for optimization of Medium 48

4.3 RESULTS AND DISCUSSION 49

4.3.1 Optimization of nutritional components of medium by

One-factor-at-a-time method 49

4.3.2 Effect of carbon sources on enzyme production 49

4.3.3 Effect of nitrogen source 50

4.3.4 Effect of Tryptophan on lactase activity 50

xiv

4.3.5 Effect of metal sources on Lactase activity 51

4.3.6 Optimization of nutritional components of medium by

Central Composite Design (CCD)

4.3.6.1 CCD for medium optimization 52

4.3.6.2 Validation of RSM Model 58

4.4 SUMMARY 59

CHAPTER-V MODELING AND OPTIMIZATION OF PHYSICAL VARIABLES

BY ARTIFICIAL NEURAL NETWORKS AND RESONSE SURFACE

METHODOLOGY

5.1 Introduction 61

5.2 MATERIALS AND METHODS 61

5.2.1 Optimization of physical factors by OFAT 62

5.3 Optimization of physical variables by RSM 62

5.3.1 Medium and shake flask experiment 62

5.3.2 Culture conditions 62

5.3.3 Prediction of variables effect on production 62

5.4 Modeling using Artificial Neural Networks (ANNs) 63

5.5 RESULTS AND DISCUSSION 63

5.5.1 Effect of incubation time on production 63

5.5.2 Effect temperature on production 63

5.5.3 Effect of inoculum size on production 64

5.5.4 Effect of pH on enzyme production 64

5.6 Optimization of physical variables by Box-Behnken Design 65

5.6.1 Validation of RSM Model 69

5.7 Development of Neural Network Model and Result Analysis 71

5.8 RSM and ANN predicted values Vs Experimental data 72

5.9 SUMMARY 72

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CHAPTER-VI CONCLUSIONS AND SCOPE FOR FUTURE WORK

6.0 Major findings from the thesis 73

6.1 Scope for future work 73

REFERENCES 75

APPENDICES

A Chemical composition for lactase assay 91

B Bacterial nutritional requirements 92

C Flow chart of the work 94

PUBLICATIONS FROM THE THESIS 95

CURRICULUM VITAE 97

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LIST OF TABLES

Table No Title Page No

1.1 Lactose intolerance prevalence in different

populations of the world 02

1.2 Characteristic features of milk allergy and lactose intolerance 04

2.1 Bacterial species reported for lactase production 12

2.2 Fungal and yeast sources for production of lactase 13

2.3 Fermentation variables for production of lactase from

various microbial sources 16

2.4 Statistical tools for enhanced lactase production from microbial species 18

3.1 Antimicrobial activity of cell-free supernatant of VUVD 001 27

3.2 Lactase activity for the isolated bacterial colonies 29

3.3 The physiological and biochemical characteristics of isolate 35

4.1 Variable ranges used in experimental design 48

4.2 The design data of CCD and lactase activity values 53

4.3 ANOVA analysis of model and medium components 54

4.4 RSM Optimized medium variables for lactase activity 58

5.1 Physical variable ranges used in experimental design 62

5.2 Actual data for design of Experiments 66

5.3 Analysis of variance of quadratic model for production of lactase 67

5.4 RSM optimized process variables for maximum lactase activity 69

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xix

LIST OF FIGURES

Figure No Title Page No

1.1 Health benefits of probiotics 05

1.2 Schematic presentation of lactose hydrolysis by lactase enzyme 07

1.3 D-Structural of lactase 08

3.1 Morphological study of VUVD001 isolate; a) pure culture

b).Gram staining 25

3.2 Spore staining of VUVD001 isolate 26

3.3 Growth curve analysis for VUVD001 26

3.4 Antimicrobial activity of VUVD001 isolate against

pathogenic strains; a) E.aerogenes and b) K.pneumonia 27

3.5 Isolation of lactase producing bacterial isolates from

dairy effluent by X-gal platting method 28

3.6 Primary screening of lactase producing activity of

VUVD001 by ONPG disc method 30

3.7 Zymogram analysis of concentrated cell free extracts of

B. subtilis VUVD001 isolate 30

3.8 IMViC test for biochemical characterization of VUVD001 31

3.9 Carbohydrate fermentation tests for VUVD001 strain 32

3.10 Starch hydrolysis and catalase test for the VUVD001 strain 32

3.11 Salt tolerance test for VUVD001 isolate 33

3.12 Thermostability test for VUVD001 34

3.13 Agarose gel analysis of PCR amplified 16srDNA;

Lane M- 1 KB ladder; Lane 1- 16S rDNA of VUVD001 36

3.14 Color key for alignment scores by BLASTN 37

3.15 Multiple sequence alignment of 16S rDNA nucleotides from species

related to Bacillus using Multalin software 44

3.16 Cluster sequencing for VUVD001 by Multalin software 44

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3.17 Dendrogram constructed by maximum-likelihood method

using MEGA 5v 45

3.18 The 16S partial sequence of B.subtilis VUVD001 strain to NCBI 45

4.1 Effect of various carbon sources on lactase production and Biomass.

a Different carbon sources of glucose, lactose, fructose, starch

and sucrose and b different concentrations of lactose in percent 49

4.2 Effect of nitrogen source on lactase production and Biomass.

a Different sources i.e., Yeast extract, sodium nitrate,

ammonium nitrate and urea and b Yeast extract concentrations

in percent 50

4.3 Effect of amino acids on lactase production and Biomass.

a Different amino acids i.e., Glycine, L-Tryptophan and L-Cysteine

and b Tryptophan concentrations in percent 51

4.4 Effect of minerals on lactase production and Biomass.

a Different minerals i.e., MgSO4, ZnSO4 and MnSO4

and b MgSO4 concentrations in percent. 52

4.5 Effect of yeast extract and lactose on lactase activity 55

4.6 Effect of tryptophan and lactose on lactase activity 56

4.7 Effect of MgSO4 and lactose on lactase activity 56

4.8 Effect of tryptophan and yeast extract on lactase activity 57

4.9 Effect of MgSO4and yeast extract on lactase activity 57

4.10 Effect of MgSO4 and tryptophan on lactase activity 58

5.1 Optimization of physical parameters for production of lactase;

a). Temperature; b). pH; c). Incubation time, h;

and d). Inoculum size, %. 65

5.2 Effect of temperature and pH on lactase production 68

5.3 Effect of temperature and incubation on the lactase production 68

5.4 Effects of incubation time and pH on the producttion of lactase 69

5.5 Output Vs. target regression plot 71

5.6 Comparison between the experimental data Vs. RSM and

ANN predicted data 72

xxi

LIST OF SYMBOLS AND ABBREVIATIONS

°C Degree Celsius

µg/ml microgram per milliliter

ANN Artificial neural networks

ANOVA Analysis of Variance

APS Ammonium persulfate

g/l gram per liter

h hour

kDa Kilo Dalton

LB Luria Bertani

mg/ml milligram per milliliter

min minute

ml milliliter

nm nanometer

OD600 Optical Density at 600 nm

OFAT One Factor at a Time

ONP o- nitrophenol

ONPG o-nitrophenyl-β-D-galactopyranoside

PAGE Polyacrylamide Gel Electrophoresis

PCR Polymerase Chain Reaction

R1 Response (lactase activity, U/ml)

RPM Revolutions per minute

sp. Species

TEMED Tetramethylethylenediamine

v/v volume by volume

VUVD Vignan University Vadlamudi

w/v weight by volume

X-gal 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside

1

CHAPTER-I

INTRODUCTION

1.1 Lactose and Lactose intolerance

The milk sugar lactose is a disaccharide which found in mammal‘s milk and important

for the sustenance of newborn infants. The lactose sweetness is low in comparison

with sucrose and it cannot be directly absorbed by the intestines. Lactose is

hydrolysed into glucose and galactose by the action of lactase during digestion

process. Lactose intolerance is malabsorption of dietary lactose due to lactase

deficiency, which is normally produced by small-intestinal mucosa. The lactase is

disappears after the weaning period of young mammals. Intestinal lactase activity was

high in most of infants during the prenatal period. But, after two to twelve years of

age, two separate groups come into view such as ―lactase non-persistence‖ group with

low lactase activity (hypolactasia) and ―lactase-persistence‖ group of people who

maintain their neonatal level of lactase activity into adulthood (Jurgen Schrezenmeir

and Michael de Vrese, 2001; Mattar et al, 2010).

Lactose intolerance is a symptomatic malabsorption of lactose which slowly reduces the

consumption of milk and other dairy products by humans. The lactose intolerance causes

various disorders namely gas, bloating, nausea, or diarrhea and non-allergic (Houts,

1988; Vasiljevic and Jelen 2001). Lactose intolerance is not limited to gut symptoms.

Systemic complaints, namely headache, vertigo, memory impairment, lethargy, muscle

and joint pains, allergy, cardiac arrhythmia, mouth ulcers, and sore throat (Matthews et

al, 2005; Harrington and Mayberry, 2008). Approximately, two-third of world population

suffers from lactose intolerance and it significantly reduces their quality of life (Alazzeh

et al, 2009). The lactase deficiency can be resolved either by using supplementary

probiotic microorganisms or using lactase in dairy products that hydrolyze lactose

present in food. This enzyme has commercial value in pharmaceutical and food

industries. Particularly, in dairy industry it is used for hydrolysis of lactose in milk or

other products like cheese and whey (Gasteiger et al. 2003). The prevalence of lactase

deficiency in the population groups in the world is shown in Table 1.1.

2

Table 1.1 Lactose intolerance prevalence in different populations of the world

(Source: Jellema et al, 2010; Rangel et al, 2016)

3

1.2 Types of intolerance

The deficiency is mainly three types such as congenital, primary and secondary. Out

of these the congenital lactase deficiency (CLD) is very severe problem and is known

as lactasia. In CLD individuals, the lactase levels are severely goes down at birth and

which is remains abnormal throughout the life. They need to take the lactose free diet

for entire life and they cannot tolerate even little amounts of lactose (Miller et al,

2000).

The primary lactase deficiency (PLD) is due to the decrease in lactase activity

between two and twenty years of age after weaning and is common in most of the

individuals. The reduction of lactase activity is probably of genetically predisposed

reasons. The symptoms of PLD includes dehydration, deprived calcium absorption,

diarrhea induced bloating etc. The watery diarrhea is one of the most dreadful

consequences of PLD depending on the amount of lactose consumed by the

individual and at the level of his tolerance (McBean and Miller, 1998; Rasinpera et al,

2006). Secondary lactase deficiency results from the damage of small and large

intestinal epithelial linings which leads to gastrectomy (Vesa et al, 2000).

1.3 Milk allergy

Milk is a basic food for infants and an important supplement in the diet of adults and

has elevated biological value consists of high nutrients. However, in few cases the

consumption of milk is associated with reactions like cow‘s milk protein allergy

(CMPA) (Bahna, 2002). The CMPA is the most common food allergy in infants and

affecting 2% to 5% of world child population with less than 3 years of age (Huang &

Kim, 2012). Cow‘s milk contains many allergens such as whey and casein

components. The whey components such as α & β lactoglobulins & bovine

immunoglobulin are allergic to children‘s. Similarly the adults are allergic to casein (α

& β) components (Eigenmann et al, 1998). The milk allergy affects the immune

system and causing symptoms like skin allergy, Food Protein-Induced Enterocolitis

Syndrome (FPIES), gastrointestinal and anaphylaxis after food ingestion (Gasparin et

al, 2010). Sometimes lactose intolerance is mystified with milk allergy in which one

or more milk proteins stimulate the immune system of the body. Table 1.2 indicates

the differences between these two diseases.

4

Table 1.2 Characteristic features of milk allergy and lactose intolerance

Characteristics Lactose intolerance Milk allergy

Definition

Difficulty in digestion of

lactose

Allergic reaction to cow‘s

milk

Cause

Low intestinal levels of

lactase

Negative immune response

for ingestion of milk protein

Dairy Food Use/

Avoidance

No need to eliminate

dairy foods, only adjust

the dose of lactose

consumed

Eliminate cow‘s milk from

the diet

Symptoms

Abdominal gas, bloating,

cramps, diarrhea

Abdominal pain, vomiting,

diarrhea, nasal congestion

and skin rash

Age Most common in adults

Common in children‘s

especially infants

Source: (Miller et al, 2000; Tumas and Cardoso, 2008)

1.4 Diagnosis

The lactose is not completely digested in the intestine of intolerance individuals and

the leftover lactose is fermented by the colonic bacteria and forms hydrogen gas. The

released gas is analyzed for diagnosis of lactose intolerance by Breath hydrogen test

and this test is appropriate for primary screening of the intolerance. Lactose

intolerance can also be diagnosed through quantification of lactase activity for biopsy

intestinal samples. This measured lactase activity is compared with the activity of

another small intestine enzyme. There are also genetic studies to find a reliable and

fast method for diagnosis of the disease (Troelsen, 2005). Urinary galactose

measurement using an enzyme strip is another method for diagnosis (Vesa et al,

2000).

1.5 Solution to lactose intolerance with probiotics

Doing away with lactose containing dietary products is not a permanent and wise

solution, considering the fact that those food supplements are quite necessary for our

health. Also, lactase enzyme has been tried in the form of drugs or encapsulations.

The people suffering from lactose mal-digestion for some unknown reason were able

to utilize yoghurt lactose but not the milk lactose. It also resulted in lower hydrogen

breath. Gradually it was found that the probiotic bacteria present in the yoghurt could

exert such beneficial trait. Milk containing L. acidophilus has shown similar results.

The non-pathogenic bacterial strains like S. thermophilus and L. bulgaricus could

produce lactase (Gallagher et al, 2004; Tuohy et al, 2003). The beneficial effects

associated with the probiotic bacteria such as Lactobacillus and Bifidobacterium on

5

human health were shown in the Fig. 1. These bacteria are used as functional

ingredients, especially in dairy products like yoghurts and other milk products.

Fig. 1.1 Health benefits of probiotics (Source: Saarela et al, 2002)

1.6 Microbial enzymes

Microbial enzymes are known to play a crucial role as metabolic catalysts, leading to

their use in various industries and applications. They are produced by living

organisms to increase the rate of an immense and diverse set of chemical reactions

required for life. They are involved in all processes essential for life such as DNA

replication and transcription, protein synthesis, metabolism and signal transduction,

etc. and their ability to perform very specific chemical transformations has made them

increasingly useful in industrial processes. The end use market for industrial enzymes

is extremely wide-spread with numerous commercial applications. The demand for

industrial enzymes is on a continuous rise driven by a growing need for sustainable

solutions (Adrio and Demain, 2005; Johannes and Zhao 2006; Kumar and Singh

2006).

6

1.7 Global market for enzymes

Enzymes are used in various sectors which include food manufacturing, cosmetics and

pharmaceutical industries. At present, almost 4000 enzymes are known, among these,

approximately 200 enzymes were originated from microbial sources. However, only

about 20 enzymes are produced on truly industrial scale. The world enzyme demand is

satisfied by about 12 major producers and 400 minor suppliers. Nearly 75% of the

total enzymes are produced by three top enzyme companies, i.e., Denmark-based

Novozymes, US-based DuPont (through the May 2011 acquisition of Denmark-based

Danisco) and Switzerland-based Roche. The market is highly competitive, has small

profit margins and is technologically intensive. According to a research report from

Austrian Federal Environment Agency, about 158 enzymes were used in food

industry, 64 enzymes in technical application and 57 enzymes in feedstuff, of which

24 enzymes are used in three industrial sectors. Almost 75% of all industrial enzymes

are hydrolytic enzymes. Carbohydrases, proteases and lipases dominate the enzyme

market, accounting for more than 70% of all enzyme sales. Many industrial processes

are already adapted the use of microbial sources for large scale production because the

chemical synthesis method have some disadvantages such as low catalytic efficiency

and stability (Demain and Adrio 2010). Internationally the industrial enzymes market

marginal value was around $105 million in 2012 but expected to grow significantly

with an average of ≥10 % per year through 2017 to reach nearly $173 (Singh et al,

2016).

1.8 Lactase

Lactase catalyzes the breakdown of a disaccharide sugar found in milk known as

lactose, into two monosaccharide sugars, galactose and glucose by the addition of a

water molecule. The oxygen bridge connecting the two sides of the lactose molecule is

cleaved and this phenomenon called as hydrolysis (Fig. 1.2). Yin et al, (2017) reported

that the microbial lactase was used as biocatalyst in industry to produce prebiotic

galactooligosaccharides(GOS) from lactose. Lactase (E.C. 3. 2. 1. 23) (β-D-

galactohydrolase, β-D-galactoside galactohydrolase, galactosyltransferase and β-

galactosidase), can catalyze both hydrolytic and transfer reactions. This enzyme also

cleaves o-glycosidic bond of other β-D galactopyranosides. The resulting sugars,

glucose & galactose are sweeter, more soluble and easily digested. Thus, lactase

increases sweetness, solubility and digestibility of the final product.

7

Fig. 1.2 Schematic presentation of lactose hydrolysis by lactase enzyme

(Source: Prescott et al, 1996)

1.9 Structural properties of lactase

Lactases produced mainly by microorganisms and are released from the cytosol as

oligomeric proteins. Lactase is composed of four polypeptide chains which is a

tetramer and each monomer contains 1023 amino acids which form the distinct

regions of structural domains with a compact three dimensional structure. The overall

structure has horizontal two-fold axis of symmetry which forms long interface, one

vertical which is activate interface and third is perpendicular. The crystal structure

was initially proposed with four asymmetrical units of tetramers. Later the structure

was elucidated with confined resolution as a single unit of tetramer, but

asymmetrically. Deletion of amino acid residue from the lactase at the vertical

position leads to weakening of active site and further dissociates into dimmers (Juers

et al, 2012).

The molecular weight of lactase is a 464 kDa as a homotetramer. Each subunit of

lactase consists of five domains and domain 1 is a jelly-roll type barrel, domain 2 and

4 are fibronectin type III-like barrels, domain 5 is a β-sandwich and domain 3 is

a TIM-type barrel. The active site is present in the third domain of lactase and made

up of elements from two subunits of the tetramer. The amino-terminal sequences of

lactase and the α-peptide involved in α-complementation. The residue in active site

region helps in stabilization of a four-helix bundle. The domains of structure were

shown with different colors in Fig. 1.3. The complementation peptide, orange; domain

1, blue; domain 2, green; domain 3, yellow; domain 4, cyan; domain 5, red. The

lighter and darker shades were used to differentiate the same domain in different

subunits. The metal ions in each of the four active sites are shown as spheres: Na+,

green and Mg2+

, blue (Matthews, 2005).

O O

OH

OH

OH

CH2OH

CH2OH

OH

OH

O

OH

OH O

OH

OH

OH

CH2OH

H2O

Lactase OH O

OH

OH

OH

CH2OH

+

Lactose D-Galactose D-Glucose

8

Fig. 1.3 3D-Structural view of lactase (source: Matthews, 2005)

1.10 Applications of lactase

Lactase main application area is dairy industry for production of low lactose

containing milk to meet the need of populations who are suffering from lactose

intolerance, sensitivity to lactose due to their lack of intestinal lactase (Albayrak and

Yang, 2002). Lactose can also be hydrolyzed using acid, but this cause‘s color

formation and fouling of the ion-exchange resins used in processing. Thus, an enzyme

has more demand in industries because enzymes are able to hydrolyze lactose without

side reactions. Low-lactose milk is produced in processing plant by supplying the

lactase to milk and also the products like lactose reduced cottage cheese, processed

cheese, ice-creams were commercially produced (McBean et al, 1998). Another use of

lactase in dairy industry is to increase sweetness and digestibility of the final product.

Since low solubility of lactose leads to its crystallization causing sandy, gritty texture

which creates trouble for some dairy product such as ice-cream, frozen milk, dry milk,

condensed and evaporated milk. Lactase also improves the utilization of high protein

supplements containing milk. Lactose conversion is also used in sweetener production

9

from whey, a biological by-product of cheese processing, since most of the lactose

pass to whey during cheese production (Qureshi et al, 2015). Lactose is utilized in

bread making because of its physiological properties such as providing suitable

texture and color. However, lactose is not fermented by ordinary baker‘s yeast. Thus,

lactase can be used since its hydrolysis products can be readily fermented and improve

the toasting of bread (Singh et al, 2016). Lactase was also used in the design of

amperometric lactose biosensors which are used for estimation of lactose in milk and

other milk based products to prevent lactose intolerance (Goktug et al, 2005).

Pharmaceutical industries use the lactase enzyme for capsulation of chewable lactase

tablets, which helps in the digestion of milk and dairy foods without gas, cramps,

bloating, or diarrhea (Nath et al, 2014).

1.11 THESIS OBJECTIVES

The objectives of the present work are

1. To isolate and screen the lactase producing bacterium from dairy industrial

effluents.

2. To characterize the lactase producing bacterium for strain identification.

3. To optimize the components of medium for the production of lactase using

One Factor at a Time (OFAT) and Response Surface Methodology (RSM).

4. To optimize process parameters using OFAT and RSM for the parsimonious

yield of lactase at laboratory scale and further modeling of physical parameters

using Artificial Neural Networks (ANN).

1.12 THESIS ORGANIZATIONS

Thesis comprised with five chapters; Chapter 1 provides the information about lactose

intolerance and lactase applications in dairy and other fields. Chapter 2 furnish the

detailed review of literature related to different sources availability for production of

lactase and methods and tools adapted in previous research works for enhanced

production of higher lactase. The chapters 3, 4 and 5 gives the information about the

methodologies and reagents used in experimental work and also provides an insight

idea about the findings of the work and discussion part. The conclusions and scope for

future work were summarized in chapter 6 and thesis is end up with references and

appendices.

10

11

CHAPTER-II

REVIEW OF LITERATURE

2.1 Sources of lactases

In the current scenario, microbial strains have employed for commercial production of

various essential enzymes. In previous studies, lactase was isolated from different

sources which include bacteria, fungi and yeast (Haider and Husain, 2007).

2.1.1 Bacteria

Microbial species was found to be efficient for large scale production of lactase as

compared to plant and animal sources (Nam and Ahn, 2011). The benefits associated

with bacterial fermentation are secretion abilities, simple fermentation, high enzyme

activity and stability, rapid growth and metabolic diversity and simple purification of

the enzyme and hence still the Bacillus strains were ideal for commercial production

of lactase (Picard et al, 2005; Prakaham et al, 2008). Lactase has been found in

various bacterial strains belonging to the Enterobacteriaceae and Pseudomonadaceae

(Tryland and Fiksdal, 1998). The dairy industry uses bacterial genera Lactobacillus

and Bifidobacterium that are capable of producing lactase enzyme and enhances the

digestion capacity in lactose intolerance patients. These bacilli are generally regarded

as safe (GRAS) and thus, the lactase secreted by them can be consumed without

excessive purification by lactose intolerance patients (Somkuti et al, 1998; Vinderola

et al, 2003; He et al, 2008). Bifidobacterium was selected as a model bacterium for

hydrolysis of lactose by the colonic microbiota (Arunachalam, 2004). Lactase

production from Bifidobacterium longum CCRC15708, B.circulans, Bifidobacterium

adolescentis, L.reteri, L.plantarum, Bifidobacterium longum B6 and Bifidobacterium

infantis CCRC14633, Lactobacillus delbrueckii, bulgaricus 11842 were also

investigated for the production of lactase (Burya and Jelen, 2000; Batra et al, 2002;

Hsu et al, 2005). The bacterial group namely Lactococci, Streptococci, Lactobacill, B.

licheniformis, B.amyloliquefaciens are well studied for relatively high quantity of

lactase (Sani et al, 1999; Husain et al, 2010). The lactase producing bacterial species

were represented in Table 2.1.

12

Table 2.1 Bacterial species reported for lactase production

I- intra cellular; E-extra cellular

2.1.2 Fungi

Fungal lactase was very sensitive to inhibition especially with high concentration of

galactose. Lactase production from fungal sources has acidic pH optima in the range

of 2.5–5.4 (Boon et al, 2000). Traditionally, lactase was obtained from Aspergillus

niger and Aspergillus oryzae because of acceptable yields from the fermentation of

these cultures (Torsvik et al. 1998; Picard et al. 2005; Kosseva et al. 2009; Bibi et al.

2014). Otieno (2010) was used thermophilic fungus namely, Talaromyces

thermophilus CBS23658 for production of intracellular lactase. The purified lactase

from Aspergillus oryzae showed optimum pH 4.5 with o-nitrophenyl β-D

galactopyranoside (ONPG) and 4.8 with lactose.

S.No Bacterial species Location Reference

1. Arthrobacter oxydans I Banerjee et al, 2016

2. L. plantarum MTCC2156 I Gobinath et al, 2015

3. L. acidophilus ATCC4356 I Milika carevic et al, 2015

4. B. animalis ssp. lactis Bb12 I Prasad et al, 2013

5. Streptococcus thermophilus I Princely et al, 2013

6. Bacillus safensi I Nath et al, 2012

7. Bacillus sp. MPTK121 - Mukesh Kumar et al, 2012

8. Citrobacter freundii E Lokuge & Mathew, 2010

9. Bacillus licheniformis ATCC

12759

E Akcan, 2011

10. Rahnella aquatilis KNOUC601 E Nam and Ahn, 2011

11. Bacillus sp. NV1 - Batra et al, 2011

12. Lactobacillus acidophilus I Choonia and Lele, 2011

13. Bacillus stearothermophilus I Chen et al, 2009

14. L. acidophilus ATCC0291 - Fung et al, 2008

15. B. longum CCRC15708 I Hsu et al, 2006

16. Saccharopolyspora erythraea E Post and Luebke, 2005

17. Lactobacillus plantarum Cytoplasm Silvestroni et al, 2002

18. Bacillus coagulans RCS3 I Batra et al, 2002

19. Bacillus circulans I Boon et al, 2000

20. Escherichia coli I Giacomini et al, 2001

21. Lactobacillus thermophilus I Greenberg et al, 1981

22. Bacillus stearothermophilus -- Pederson, 1980

23. Leuconostoc citrovorum I Singh et al, 1979

13

2.1.3 Yeasts

The yeast species, Kluyveromyces lactis and Kluyveromyces marxianus and

Saccharomyces fragilis used as sources for production of lactase (Shaikh et al, 1997;

Santos et al. 1998). The Kluyveromyces lactis is still the major commercial source for

production of lactase and its major drawback is lower thermo stability (Chen et al,

2009). Yeast lactases are most active in the buffers of pH

6.0–7.0 (Genari et al, 2003).

The recent reports on the lactase production from different species of fungi and yeast

were listed in the Table 2.2.

Table 2.2 Fungal and yeast sources for production of lactase

Yeast species Location Reference

Kluyveromyces marxianus CCT7082 E Machado et al, 2015

Kluyveromyces sp. CK8 E Am-aiam et al, 2015

Kluyveromyces marxianus CCT7082 E Manera et al, 2008

Kluyveromyces lactis NRRL Y-8279 I Dagbagli, 2008

Kluyveromyces fragilis Cell

bound

Matioli et al, 2003

A.niger ATCC9142 E Kazemi et al, 2016

Aspergillus tubengensis GR-1 E Raol et al, 2014

Aspergillus oryzae - Shankar et al, 2007

Absidia sp.WL511 I Li et al, 2006

I- intracellular; E- extracellular

2.1.4 Plant and Animal sources

Many plants like Rosaceae, almonds, wild roses, soy bean seeds and coffee and

peanuts were reported for lactases, which are essential plant growth and fruit ripening.

The lactase from papaya source was used in hydrolysis of its cell wall and softening of

the fruit (Lopez et al, 2001; Bryant et al, 2003; Lazan et al, 2004). Lactase activity

levels at different developmental stages in the cell wall of strawberry and in peaches,

apricots, apples (Fragaria ananassa) fruits were also reported (Hadfield et al, 2000;

Tateishi et al, 2001; Nagy et al, 2001; Flood and Kondo, 2004; Haider and Husain,

2007).

The lactase enzyme has also been found in intestine of various animals like dogs,

rabbits, calves, sheep, goats, rats, rams, bulls, boars (Wallenfels et al, 1960).

Moreover, numerous studies have shown the presence of enzyme in human saliva,

14

distribution of lactase in fetuses of primates and farm animals and in tissues of rats

and mice and in plasma serum (Heilskov et al, 1951).

2.2 Optimization of nutritional components and physical factors for production

of lactase

The cost of industrial enzymes primarily depends on method of fermentation and

medium used for production. Hence, the production was enhanced through the design

of medium consist of special inducers. Usually, defined medium was not established

for the maximum production of any enzyme due to genetic diversity of various

microbial sources. Each strain has its own favorable conditions for higher yields.

Therefore, study of preferable conditions and suitable for a newly isolated strain is an

important phenomenon in bioprocess industries to achieve the desirable production

(Prakasham et al, 2005). The various chemical and physical variables namely, carbon,

nitrogen and other supplementary sources like amino acids and metal ions and pH,

temperature, rpm , dissolved oxygen (DO) may influence the production. The

component of medium has a critical role in multiplication of cell number and to attain

higher production. The activity of enzymes was affected by different parameters such

as type of strain, cultivation conditions and the ratio of carbon and nitrogen sources in

medium (Jurado et al, 2004). Therefore, the notable fermentation variables were

studied for optimization of lactase production through submerged fermentation

(Schneider et al, 2001; Jurado et al, 2004). Most of industrial lactases are produced by

liquid fermentation in comparison with other fermentation methods.

2.2.1 Medium components effect on the lactase production

Alazzeh et al (2009) described that the carbon source in fermentation media is primary

energy source and essential for growth and production of lactase and it also regulates

the biosynthesis of lactase. Rezessy et al (2002) has studied the effect of various

sugars such as galactose, melibiose, stachyose and raffinose on production of lactase.

Duffaud et al (1997) was examined the effect of complex carbon sources namely,

locust bean gum and guar gum on the production of lactase. Marisa et al (1996) was

found that the stachyose was the favorable inducer for lactase production through

cultivation of Lactobacillus fermentum. Similarly, Guar gum as carbon source was

also reported to raise the lactase production through Bacillus megaterium VHM1

(Patil et al, 2010). The probiotic strain Lactobacillus fermentum CM33 in submerged

process yielded high production when the medium was supplemented with lactose

(Sriphannam et al, 2012).

15

Shaikh et al (1997) reported that the organic and inorganic nitrogen sources provide

the favorable condition for production of lactase. Lokuge and Mathew (2010) have

reported the nitrogen source soya bean meal highly enhanced the production of lactase

by Geobacillus sp. The organic nitrogen sources such as peptone, tryptone and yeast

extract have been studied for their effect on production by Bacillus

stearothermophilus (Delente, 1974). In other hand, the combinational effect of yeast

extract with ammonium sulphate was also studied for enhanced production of lactase

by Bacillus megaterium VHM1 (Patil et al, 2010). Apart from carbon and nitrogen

sources, the ionic components are also highly essential for enhanced production of

lactase. The molar concentration of ions like Ca2+

, Mg2+

, Na+, NH

4+ and K

+ may

influence the production and stability of lactase (Garman et al, 1996). Similarly, Liu

et al, (2007) was found that the minerals like CaCl2, K2HPO4 and MgSO4 and

vitamins such as vitamin C and B shown the high effect on production.

2.2.2 Physical parameters effect on production of lactase

The fermentation process parameters such as inoculums size, temperature, pH,

incubation time and agitation etc., may affect the yield. Therefore, optimization of

factors is the primary step in designing the process for improving production. Growth

of cells and production of lactase was strictly depends on the pH of the medium. The

optimum pH range 6.0 to 7.5 highly preferred for lactase production from bacterial

fermentation (Kamaran et al, 2015). The neutral pH for optimum production of lactase

as reported through submerged fermentation by E.coli (Nagao et al, 1988) and

L.curvatus R08 (Yoon and Hwang, 2008). Lactase from Kluyveromyces marxianus

was produced at pH 3 through submerged fermentation (Al-Jazairi et al, 2015). The

favorable temperature for production of enzymes varied based on type of organisms.

Most of the probiotic organisms like Lactobacillus and Bfidobacterium species were

grown at 30-37 °C for lactase production (Xiao et al, 2000). Rezessy-Szabo et al

(2003) examined the lactase production from Thermomyces lanuginosus and it can

grow easily on yeast phosphate soluble starch (YpSs) medium at 45 ºC. Optimum

growth occurs between 45-50 ºC. No growth is observed at temperatures either below

30 ºC or above 60 ºC. The optimum temperature 30 °C for lactase production by the

cultivation of by lactobacilli strain was reported (Murad et al, 2011). Mixing is

required for uniform distribution of nutritional components of medium to the growing

cells. The mixing conditions were varied based on type of the organism and design of

the vessels used. Maximum production of lactase was attained at 180 rpm in shake

16

fermentation from Bacillus megaterium VHM1 (Patil et al, 2010). Different process

parameters for production of lactase from various sources were listed in Table 2.3.

Table 2.3 Fermentation variables for production of lactase from various microbial

sources

Microorganism Carbon Nitrogen pH Tem

p.

°C

RP

M Incubat

ion

time, h

Activity,

U/ml

Reference

Lactobacillus

delbruekii

subsp. bulgaricus

Lactose peptone 7.2 37 - 48 7.69 Mazhar Ali et

al, 2016

Lactobacillus

acidophilus ATCC

4356

Lactose Yeast

extract and

Meat extract

6.5–

7.5.

37 150 48 1.05 MilicaCarevic

et al, 2015

Streptococcus

thermophilus

Lactose Whey 7.2 40 - 24 7.76 Princely et al,

2013

B. animalis ssp.

Lactis Bb12

Lactose Yeast

extract and

peptone

6.8 37 180 18 6.84 Prasad et al,

2013

Bacillus sp. Lactose peptone 7.0 35 200 48 0.294 Jayasree

natarajan et al,

2012

Bacillus megaterium

VHM1

Guar

gum

Peptone 7.5 50 180 28 1.6 Patil et al,

2010

Lactobacillus

curvatus R08

Raffino

se

Peptone,

Yeast

extract and

Meat

extract

7.0 35 - 12 40 Yoon and

Hwang, 2008

Leuconostoc

mesenteriodes JK55

Raffino

se

Peptone,

Yeast

extract and

Meat

extract

7.0 35 - 12 40 Yoon and

Hwang, 2008

Bacillus

stearothermophilus

soybean Yeast 6.5–

7.0

60 - 16 1.08 Gote et al,

2004

Meal extract

Lactobacillus

delbrueckii subsp.

bulgaricus ATCC

11842

Lactose Yeast

extract

5.6 43 150 48 3.09 Vasiljevic and

Jelen, 2001

Citrobacter freundii Raffino

se

Peptone 8.0 30 115 12 14 Lokuge et al,

2000

Escherichia coli Raffino

se

Peptone 7.0 30 115 6 1.2 Lokuge et al,

2000

17

2.3 Statistical tools for improved lactase production from microbial sources

Many researchers were tried to enhance the yield by adapting a statistical approaches

like Taguchi and RSM. The statistical studies for design of experiments using the

tools like RSM, orthogonal array, artificial neural network and genetic algorithms are

well recognized for providing the economically feasible solutions for optimization in

bioprocessing sector. The classical optimization method was time taking and it cannot

give a complete picture of independent factors affecting the production and also the

combinational interaction of variables on process could not be understood. But in

Response Surface Methodology (RSM), the effect of independent variables and their

interactions on yield were predicted (Sreenivas Rao et al, 2004; Prakasham et al,

2005; Ravichandra et al, 2007).

2.3.1 Response Surface Methodology (RSM)

RSM is an acquisition of statistical and mathematical techniques which are used to

describe the relative effects between process output and variables in process. RSM is a

more convenient tool for designing experiments, plotting models, evaluating the

effects of factors and exploring optimum conditions of factors for significant

responses. RSM is also used for optimization of prominent varieties of fermentation

media and studying interactions among various bioprocess parameters with the

minimum number experiments (Amid et al, 2011; Sanjivani et al, 2016). RSM

involves assumptions to describe the relative influence of variables for the objective

function and the frequent model employed for optimization was second order

polynomial. RSM model also generates mathematical equation based on the

experimental data (Basri et al, 2007; Bas and Boyaci, 2007). Banerjee et al (2016)

studied the lactase production through the cultivation of bacterium Arthrobacter

oxydans using Box-Behnken design. The optimum production of lactase was found at

pH, 6.76; temperature, 36.1 °C and rpm, 121.37. Am-aiam et al (2015) reported that

the highest lactase activity was produced from the cultivation of thermotolerant yeast

achieved through optimization nutritional variables of the medium consists of yeast

extract, (NH4)2SO4, KH2PO4, lactose and MgSO4.7H2O by a central composite design

(CCD). The statistical tools employed for the production of lactase from different

microbial sources were represented in Table 2.4.

18

Table 2.4 Statistical tools for enhanced lactase production from microbial species

S.N

o

Organism I/

E

Fermentat

ion Type

Tool

used

Variables in

model design

Activity Reference

1 Bacillus sp.

LX

E Submerged RSM galactose,

peptone ,

MnSO4

1.06 Jaekoo Lee et

al, 2013

2 S.griseoloalbu

s

E Submerged RSM carbon source,

yeast extract,

MgSO4, FeSO4

and salinity

50 Anisha et al,

2008

3 Lactobacillus

fermentum

CM33

- Submerged RSM lactose,

tryptone and

Tween8

4.98 Sriphannam et

al, 2012

4 Aspergillus

foetidus ZU-

G1

E Submerged RSM soybean meal,

wheat bran,

KH2PO4,

64.75 Liu et al, 2007

FeSO4.7H2O

and the

medium initial

5 Kluyveromyce

s marxianus

CCT 7082

E Submerged RSM pH, Lactose,

Glycerol

31.8 Machado et al,

2015

6 Kluyveromyce

s sp. CK8

E Submerged RSM yeast extract,

lactose,

(NH4)2SO4,

KH2PO4

and

MgSO4.7H2O

8.82 Am-aiam et al,

2015

7 Kluyveromyce

s marxianus

CCT7082

E Submerged RSM Lactose, Yeast t

extract)

And

((NH4)2SO4)

10.6 Manera et al,

2008

I- intracellular; E- extracellular

2.3.2 Artificial neural networks (ANN)

In practice, both in RSM and orthogonal array tools have limitation towards the

variable level in plotting of variables on response (Fang et al, 2003). To overcome

these problems artificial Feed-Forward Neural Networks (FFNN) and genetic

algorithms (GA) were used. ANN model was the well established and fashionable tool

in statistical analysis of experimental data and also used to understand the

biotechnology applications like expression function, functional analysis of genomics

and proteomics. The ANN approach has the wide applications in solving nonlinear

19

problems. An artificial neural network was extremely connected network structure

consisting of many processing elements which are capable of performing parallel

computation for data processing (Manohar et al, 2005). In the literature survey, it was

observed that many researchers were carried out their studies on comparison of ANN

and RSM to predict accuracy of the models (Arulsudar et al, 2005). Some researchers

used hybrid GA-FNN for optimization studies. Hanai et al (1999), Hongwen et al

(2005), Nagata and chu (2003) and Kazemi et al (2016) were conducted the research

on optimization study by using Orthogonal arrays design for production of lactase

through A.niger ATCC9142 by solid state fermentation. They reported the high

significant range of variables, which includes the solid substrates (wheat straw, rice

straw and peanut pod), carbon/nitrogen (C/N) ratios, incubation time and inducer. The

yeast species, Kluyveromyces marxianus capable of producing homologous enzymes,

such as β galactosidase, as well as heterologous proteins and also can grow on

different substrates including lactose as the sole carbon and energy source (Furlan et

al, 2000; Martins et al, 2002; Ribeiro et al, 2007). The lactase production from

Aspergillus niger MRSS234 through the screening of various nitrogen sources,

minerals and enzyme inducers by statistical method was reported (Srinivas et al,

1994). In solid state fermentation, the lactase production was investigated by

statistical approach through screening of process variables and their optimization from

Aspergillus foetidus ZU-G1 (Liu et al, 2007) and Streptomyces griseoloalbus (Anisha

et al, 2008). To the best of our knowledge no reports were available for optimization

of different variables for extracellular lactase production from Bacillus subtilis. Many

studies reported on production of lactase from fungal sources across the world, but till

date not many studies was reported on lactase enzyme produced by Bacillus sp. for

higher yields. Hence, the effort has been made for improvement of the lactase

production. In present study, screening and isolation followed by identification has

resulted in a new strain of Bacillus sp. that produces higher lactase which is discussed

in the later part of this thesis.

2.4 MOTIVATION

Reducing the lactose intolerance has thrown a challenge for researchers, because 65%

of human adults (and most adult mammals) were surveyed as lactose intolerants due

to the lack of the secretion of intestinal lactase after weaning. To overcome from

lactose intolerance the dairy and other milk products were supplemented with lactase

producing strain or through the large scale production. There are few reports and

20

literature available on the production of lactase from bacteria. Lactase has excellent

application in dairy industry for hydrolysis of lactose and can reduce the intolerance.

Hence, the importance of the study is to explore microbial sources to make

supplements available for genetically predisposed lactose intolerant subjects.

¶

21

CHAPTER-III

BIOCHEMICAL AND MOLECULAR CHARACTERIZATION OF

LACTASE PRODUCING BACTERIUM

3.1 INTRODUCTION

The dairy effluents are fully occupied with nutrients, lactose and other sanitizing

agents which facilitate a suitable environment for rapid population of microbial

species. The bacterial species present in dairy effluent has proved for probiotics and

these species were highly preferred for production of lactase (Sreekumar and

Krishnan, 2010). So far, most of the probiotic and prebiotic bacterial oligosaccharides

have been used in combination with dairy products, since these products often contain

large amounts of lactose. Much attention has been focused on the lactase, which

involved in the bacterial metabolism of lactose. In addition to normal hydrolysis of the

β-D-galactoside linkage in lactose and lactase also catalyze the formation of

galactooligosaccharides through transfer of one or more D-galactosyl units onto the

D-galactose moiety of lactose. This transgalactosylation reaction has been shown to be

a characteristic of lactase enzyme from a variety of bacterial and fungal species

(Moller et al, 2001). Picard et al (2005) reported that the bacterial lactase has highly

preferred for hydrolysis of lactose due to their superior activity and stability. From

the past three decades many researchers proved that lactase has found use in numerous

applications such as dairy, confectionary, baking and soft drinks industries (Grosova

et al, 2008; Panesar et al, 2010).

Ladero et al (2001) studied the biochemical parameters like stability and activity of

lactase from E.coli using ONPG as substrate. Sharma et al (2014) was isolated and

screened the lactase producing bacterial species by X-gal plate method and activity of

the lactase was measured by ONPG assay. In previous studies many researchers were

used the 16S rRNA technique for identification of bacterial strains due to its reliability

(Macrae et al, 2000; Cardinale et al, 2004). The strain identification of bacteria that

exist in the environment has been based on the determination of 16S rRNA sequences

of amplified genes (Mota et al, 2005).

22

3.2 MATERIALS AND METHODS

3.2.1 Morphological characterization of lactase producing isolate

The cell morphology and motility of isolate were examined by light microscopy.

Spore-staining was done on the basis of Schaeffer and Fulton‘s method. Smears were

made with 56 h old cultures and fixed with gentle heat. The smear was immersed in

malachite green stain for 3 to 6 min at 37 °C in dark followed by washing with tap

water and counter stained with Schaeffer and Fulton spore stain-B for 30 s. The smear

was air dried and viewed under a microscope with 100x resolution (Miller, 1972).

3.2.2 Growth curve

The flask contains 100 ml of lactose broth medium and was inoculated with 5 ml of

the inoculum for growth analysis. The flask was kept for incubation in orbital shaker

at 37 oC and speed of 160 rpm. For every 3 h, 5 ml of the culture medium was taken

and the absorbance was measured at 600 nm using calorimeter against an uninoculated

blank and the same step was repeated till the cell growth had reached to stationary

phase (Kumar et al, 2014)

3.2.3 Antimicrobial activity by agar diffusion method

The antimicrobial activity of isolate was screened on agar plate and was overlaid with

25 ml of overnight active culture. Different wells were made in agar plate and were

crammed with 100 micro liters cell free supernatant of overnight culture obtained by

centrifuging broth at 15000 rpm, 4 °C for 20 min. The plates were sealed with

parafilm and incubated at 37 oC for 24 h in incubator. The diameter of inhibition zones

extending across the well was measured and clear zone of more than 1 mm was

considered as positive inhibition (Asimi et al, 2013).

3.3 Biochemical characterization

3.3.1 X-gal plate method

Dairy effluent sample was collected from Sangam Dairy industry, Vadlamudi village,

Guntur (district), Andhra Pradesh, India. The sample was serially diluted and plated

on lactose agar medium containing contains 50 μg/ml of 5-bromo-4-chloro-3-idolyl-β-

D-galactopyranoside (X-gal) for isolation of lactase producing bacterial isolates. The

plates were incubated for 24 h at room temperature. The blue colonies on the plate

were subcultured on nutrient agar medium by streak plate method (Maity et al, 2013).

23

3.3.2 Lactase enzyme assay

The lactase activity was determined using o-nitrophenyl-β-D-galactopyranoside

(ONPG) as substrate. The ONPG solution was prepared with phosphate buffer and

used for assay. 0.5 ml of enzyme source was added with 2.0 ml of substrate and

incubated for 30 min. The reaction was stopped with addition of 0.5 ml of 1 M

Na2CO3 and absorbance was recorded at 420 nm. Activity of lactase was determined

from ONP standard graph. One unit of activity defined as amount of enzyme that

liberates 1 micromole of ONP from the substrate per min under assay conditions

(Ghosh et al, 2012).

3.3.3 ONPG disc assay

The lactase producing ability of VUVD001 isolate was tested by using ONPG discs

(Kalogridou-Vassiliadou, 1992).

3.3.4 Zymogram Analysis for Lactase Activity

Zymogram assay for cell free extract of isolated strain was performed using native-

PAGE method with 10% (w/v) polyacrylamide gel. After electrophoresis the gel was

incubated in 0.25 mM X-gal solution at room temperature for 10 min and the

hydrolysis of X-Gal was confirmed by the formation of blue band on the gel (Trimbur

et al, 1994).

3.3.5 IMViC, Sugar fermentation and enzyme tests

The IMViC test was done for determination of biochemical properties of the

VUVD001 isolate. The sugar fermentation test for different sugars like lactose,

glucose, sucrose, maltose and starch was performed to determine the metabolization

ability of VUVD001 isolate. The VUDV 001 was also tested for enzyme activity of

catalase and amylase (Princely et al, 2013).

3.3.6 Salt tolerance test

Salinity test was carried out to determine the ability of isolate in a salt-rich

environment. The sterile nutrient broth medium was prepared with different NaCl

concentrations like 2%, 4%, 6%, 8% and 10%. Then, the tubes were inoculated with

one loop of VUVD001 culture and incubated for 24 h at 37 °C in shaking incubator.

The biomass absorbance was measured at 600 nm using calorimeter (Patil, 2014).

24

3.3.7 Thermo stability test

Thermo stability test was done to determine the survival efficiency of the bacterium at

high temperature. A loop full culture of VUVD001 isolate was streaked onto the agar

medium. This plate was incubated in inverted position for 24 h at different

temperatures range from 20 to 55 oC in an incubator (Panda et al, 2013).

3.4 Molecular characterization of the lactase producing isolate

3.4.1 Bacterial DNA isolation

The standard protocol of Hoffman and Winston was used for bacterial DNA extraction

(Hoffman and Winston, 198) Lactase producing colony was inoculated in nutrient

broth and was grown for 36 h at 37 ºC. The cells were collected from 5 ml of the broth

culture. Lysozyme (100 μl) was added and incubated at room temperature for 30 min,

followed by the addition of 800 μl cell lysis buffer (1X TE buffer (pH-8.4); 1 % SDS

and 100 µg proteinase K). Nucleic acids was separated with two rounds of extraction

with phenol: chloroform: isoamylalcohol (25:24:1) mixture. The aqueous layer

containing genomic DNA was separated and then 800 μl of isopropanol was added on

top of the solution. The two layers were mixed gently to precipitate the genomic

DNA. Genomic DNA was then harvested by centrifugation at 12,000 rpm for 10 min

at 4 oC. Pellet was washed with 70% ethyl alcohol. The DNA pellet was air dried and

dissolved in 50 µl of 1X TE buffer. The quality of DNA was determined by running

on 0.8% agarose gel stained with ethidium bromide (Sen et al, 2010).

3.4.2 PCR amplification

PCR reaction was done in a gradient thermal cycler (Eppendorf, Germany). The

universal primers 27F and 1429R were used for 16S rDNA fragment amplification.

The reaction mixture of 100 ng genomic DNA, 2.5 Units Taq DNA polymerase, 5 μl

of 10X PCR amplification buffer, 200 μM of dNTPs, 10 pico moles each of the two

universal primers and 1.5mM MgCl2. Amplification was done by initial denaturation

for 3 min at 94 ºC and then subjected to 40 cycles of denaturation at 94 °C for 30 s,

annealing at 55 ºC for 30 s and elongation at 72 °C for 2 min and a final extension

conducted at 72 °C period of 10 min. The amplicon of 16S rDNA was confirmed by

agarose gel electrophoresis and visualized on UV transilluminator by staining with

ethidium bromide (Sen et al, 2010).

25

3.4.3 DNA sequencing

DNA sequencing was performed at Helini Biomolecules Pvt Ltd at Chennai, India.

Obtained DNA sequences were subjected to BLAST analysis through Pub Med

database (http://www.ncbi.nlm.nih.gov/blast) using the algorithm BLASTN and

compared with other sequences to analyze bacterial strain and its phylogeny.

3.5 RESULTS AND DISCUSSION

3.5.1 Morphological characterization

3.5.1.1 Gram staining

The VUVD001 isolate was sub-cultured on nutrient agar plates by streak plate method

to acquire pure culture (Fig.3.1a) and the same culture was examined for microscopic

observations. The morphology of the VUVD001 was observed as Gram-positive and

rod shaped (Fig. 3.1b).

Fig. 3.1 Morphological study of VUVD001 isolate; a) pure culture b) Gram staining

3.5.1.2 Spore staining

Spore production was the vital characteristic feature of a few microorganisms.

Bacterial spores are microbial cysts which can exist in the unfavorable conditions like

heat, dehydrated and radiation state. The spores of B. subtilis species were being used

as probiotics and competitive exclusion agents for human consumption (Casula and

Cutting, 2002). Schaeffer and Fulton‘s spore stain was showed the positive results for

isolate VUVD001 which produced the light green spores (Fig.3.2).

26

Fig.3.2 Spore staining of VUVD001 isolate

3.5.1.3 Growth curve

The growth profile of B.subtilis VUVD001 isolate was studied at different time

intervals up to 48 h. During incubation period, the concentration cell mass was

measured by taking an absorbance for every 3 h. The production of lactase enzyme

from cultivation of B.subtilis VUVD001 strain was started after 9 h and then lactase

activity was improved as the growth of cell increased up to 36 h. The maximum

lactase activity was found to be 15.13 U/ml after 36 h of incubation and beyond this

point, the growth and activity was slowly reduced upto 48 h (Fig.3.3).

0 3 9 12 15 18 21 24 27 30 33 36 39 42 45 48

0

5

10

15

20

0.0

0.2

0.4

0.6

0.8

OD Activity

Incubation Time, h

Act

ivit

y, U

/ml B

iom

ass OD

Fig. 3.3 Growth curve analysis for VUVD001

27

3.5.1.4 Antimicrobial activity

The cell-free supernatant of VUVD001 isolate was tested for antimicrobial effect

against the pathogenic test organisms. The maximum activity was observed on the

Enterobacter aerogenes MTCC111 and Klebsiella pneumonia MTCC109 (Fig.3.4).

The minimal activity was found against Staphylococcus aureus MTCC96. The zones

of inhibition on the growth of selected pathogenic strains were shown in the Table 3.1.

Table 3.1 Antimicrobial activity of cell-free supernatant of VUVD001

Test organism Zone diameter in mm

20 μl 50 μl 100 μl

E. aerogenes MTCC111 15 21 26

K. pneumonia MTCC109 14 16 22

S. aureus MTCC96 - 10 10

Fig. 3.4 Antimicrobial activity of VUVD001 isolate against pathogenic strains; a)

E.aerogenes and b) K.pneumonia

3.6 Biochemical characterization

3.6.1 X-gal plate method

The lactase producing bacterial colonies were isolated from dairy effluent by X-gal

plating method. A total of 46 isolates obtained from dairy effluent, a highest lactase

producing bacterium, named as VUVD001 (working sample id) was selected for the

study (Fig.3.5). Sreekumar and Krishnan (2010) were also reported that the X-gal

method for screening of lactase producing bacterial isolates. Maity et al (2013) were

also isolated the lactase producing bacterial strains from soil sample.

28

Fig. 3.5 Isolation of lactase producing bacterial isolates from dairy effluent by X-gal

plating method

3.6.2 Lactase enzyme assay for blue bacterial isolates

Among all blue isolates the VUVD001 produced highest activity and was found to be

15.10 U/ml after 36 h incubation. The activity of lactase positive isolates was showed

in Table 3.2. Our experimental results showed the improved lactase activity compared

with other previous results. Arekal et al (2014) reported that highest lactase activity of

10.6 U/ml was obtained with Lactobacillus plantarum MTCC 5422 in the soy whey

based medium at optimized conditions of incubation time 33 h, pH 6.6 and

temperature 37 °C. Hsu et al (2005) reported the maximum lactase activity of 18.6

U/ml was obtained in submerged fermentation with Bifidobacterium longum CCRC

15708. Based on biochemical characterization, it was interpreted as the isolate was

positive for lactase activity.

29

3.2 Lactase activity for the isolated bacterial colonies

S.No Strain name Activity, U/ml

1. VUVD001 15.10

2. VUVD 002 12.23

3. VUVD 003 11.35

4. VUVD 004 10.26

5. VUVD 005 7.64

6. VUVD 006 7.41

7. VUVD 007 6.45

8. VUVD 008 9.54

9. VUVD 009 2.68

10. VUVD 010 3.67

11. VUVD 011 4.42

12. VUVD 012 5.24

13. VUVD 013 3.47

14. VUVD 014 4.75

15. VUVD 015 5.67

16. VUVD 016 6.47

17. VUVD 017 6.45

18. VUVD 018 4.27

19. VUVD 019 7.84

20. VUVD 020 1.86

21. VUVD 021 5.67

22. VUVD 022 5.74

23. VUVD 023 4.26

S.No Strain name Activity, U/ml

24. VUVD 024 3.12

25. VUVD 025 4.26

26. VUVD 026 5.27

27. VUVD 027 4.26

28. VUVD 028 3.54

29. VUVD 029 4.26

30. VUVD 030 4.26

31. VUVD 031 3.47

32. VUVD 032 5.24

33. VUVD 033 6.10

34. VUVD 034 5.22

35. VUVD 035 3.84

36. VUVD 036 7.45

37. VUVD 037 6.41

38. VUVD 038 5.31

39. VUVD 039 4.28

40. VUVD 040 3.64

41. VUVD 041 5.24

42. VUVD 042 12.20

43. VUVD 043 6.23

44. VUVD 044 4.26

45. VUVD 045 2.67

46. VUVD046 4.65

3.6.3 Qualitative assay for VUVD001 by ONPG disc method

In our study lactase producing bacterial strain VUVD001 was screened by ONPG disc

method and observed that the reaction mixture turned to deep yellow color after

incubation indicating that our bacterial strain has ability to hydrolyze ONPG into ONP

(Fig.3.6). Favier et al (2011) reported similar method in the biochemical screening of

Bifidobacteria for lactase activity.

30

Fig. 3.6 Primary screening of lactase producing activity of VUVD001 by ONPG disc

method

3.6.4 Zymogram Analysis

Lactose hydrolyzing enzyme activity in crude cell free extracts of B.subtilis

VUVD001 strain was confirmed through zymogram assay. The native PAGE gel was

stained with chromogenic X-gal. The enzyme performed hydrolysis of X-gal which

was observed in gel with distinct blue color band (Fig. 3.7). Previous reports have

been revealed that the X-gal hydrolysis through zymogram assay was used for

confirmation of lactase producing nature of a cold-adapted bacterium named as

Rahnella aquatilis KNOUC601 (Nam et al, 2011).

Fig.3.7 Zymogram analysis of concentrated cell free extracts of B. subtilis VUVD001

isolate. Lane 1 & 2 represents X-gal hydrolyzing enzyme from VUVD001 grown at

different temperatures (37 oC & 35

oC); Lane 3 – positive control (Lactobacillus sp.

ATCC-8008); Lane 4 – Negative control (B. cereus ATCC-10876); Lane 5- blank.

31

3.6.5 IMViC test

The yellow color in the tube is an indicative that the VUVD001 strain is negative for

indole test (Fig. 3.8a). Upon the addition of two drops of methyl red reagent there is

no color change the culture tube that indicates that the isolate is negative for methyl

red test (Fig. 3.8b). Voges-Proskauer test was performed to differentiate organism

based on the ability of isolate to produce acetylmethylcarbinol product from glucose

fermentation. The overnight culture medium of isolate was turned into reddish color

on addition of Voges-proskauer reagents. It indicates that the isolate was positive and

no color change was observed in negative control, E.coli and blank. Hence, the results

support that isolate was positive for acetylmethylcarbinol (Fig. 3.8c). Citrate

utilization test was performed to determine the ability of isolate to utilize sodium

citrate as carbon source and inorganic ammonium di hydrogen phosphate (NH4H2PO4)

as nitrogen source. After four days of incubation the isolate color on agar medium was

changed from green to blue. Hence, the isolate successfully utilizes sodium citrate and

ammonium di hydrogen phosphate for growth (Fig. 3.8d). Similar studies were

conducted by Abiola and Oyetayo (2016) for the biochemical characterization of

bacterial species.

Fig.3.8 (a)-(d) IMViC test for biochemical characterization of VUVD001

3.6.6 Carbohydrate utilization test

The VUVD001 isolate shown positive result for utilizing various carbon sources

namely glucose, lactose, fructose, sucrose and starch by changing the color of phenol

red broth medium from red to yellow due to change in pH of medium after 24 h of

incubation. Based on this observation it was found that the VUVD001 strain was

successfully utilized these sugars as carbon source for growth and also no gas was

trapped into Durham tube (Fig.3.9).

32

Fig. 3.9 Carbohydrate fermentation tests for VUVD001 strain; a) glucose, b) lactose,

c) fructose, d) sucrose and e) starch

3.6.7 Starch hydrolysis and catalase test

The isolate was also screened for amylase by starch hydrolysis. The yellow colored

plate shown that the isolate was positive for amylase and the bacterium E.coli was

negative control for amylase (Fig. 3.10a&b). The formation of bubbles was observed

by placing loop full culture of isolate on a glass slide containing few drops of

hydrogen peroxide. It means that isolate produces catalase enzyme and it splits

hydrogen peroxide into water and oxygen, hence the isolate was catalase positive (Fig.

3.10c).

Fig. 3.10 (a)-(c) Starch hydrolysis and catalase test for the VUVD001 strain

33

3.6.8 Salt tolerance test

The growth efficiency of isolate in salt rich environment was studied by salt tolerance

test and isolate was able to survive at high salt concentration (8% NaCl). The

maximum growth was observed with 4% (w/v) sodium chloride concentration

(Fig.3.11). The mineral sources may influence the proliferation rate of

microorganisms. Hudaaneetoo et al (2015) reported that the lactic acid bacterium was

able to tolerate up to 4% NaCl concentrations. Here, our isolated strain has shown

significant growth up to the concentration of 6% NaCl. Similarly, in the earlier studies

the salts like NaCl and MgCl2 were proved as growth influencing factors (Pulicherla

et al, 2013).

Fig. 3.11 Salt tolerance test for VUVD001 isolate

3.6.9 Thermo stability test

From the results of thermo stability test it was observed that the VUVD001 isolate

was able to survive at 55 oC. The plate has shown growth of VUVD001 after 24 h of

incubation in an incubator (Fig.3.12). The physiological and biochemical

characteristics of VUVD001 isolate was represented in Table 3.3.

34

Fig.3.12 Thermostability test for VUVD001

35

Table 3.3 The physiological and biochemical characteristics of isolate

Tests VUVD001 isolate

Gram staining +

Spore staining +

Growth at different pH

5 +

6 +

7 +

8 +

9 -

Growth on NaCl %

2 +

4 +

6 +

8 +

10 -

Growth at temperatures ( oC)

20 +

25 +

30 +

35 +

40 +

55 +

Starch hydrolysis +

Indole test -

Methyl red test -

Voges-proskauer +

Citrate utilization +

Oxidation–fermentation Fermentative

Aerobic growth +

Lactase +

Catalase +

36

3.7 Molecular Characterization and identification of VUVD001

3.7.1 Amplification

The PCR amplification of ribosomal DNA was amplified with universal primers of

rDNA and produced amplicon of 1400 bp was obtained (Fig.3.13). Since DNA

sequencing was performed directly on PCR amplicon, a 1000 bp DNA was obtained

after merging forward and reverse sequencing reads. This highly conserved region of

16S rDNA was identified by blast search and multiple sequence alignment was done

with sequences of other Bacillus species obtained from GenBank.

Fig. 3.13 Agarose gel analysis of PCR amplified 16S rDNA; Lane M- 1 KB ladder;

Lane 1- 16S rDNA of VUVD001

3.7.2 Multiple sequence alignment (MSA)

The 16S rDNA sequence of VUVD001 was blasted through PubMed database using

the algorithm BLASTN to figure out the highly conserved and similar 16S rDNA

sequences (Fig. 3.14).

37

Fig. 3.14 Color key for alignment scores by BLASTN

The MSA was performed for the selected set of bacterial 16S partial sequences by

Multalin software version 5.4.1 and the similarity among the partial sequences of

selected bacterial species were showed in the alignment (Fig. 3.15). The species

obtained from GenBank database are as follows: Bss (Bacillus subtilis strain DBT13),

Bma (Bacillus malacitensis strain LMG22477), Bvs (Bacillus vallismortis strain

NBRC101236), Bas (Bacillus vallismortis strain NBRC101236), Bns (Bacillus

nematocida strainB-16), Bsi (Bacillus siamensis strain PD-A10), Bls (Bacillus

licheniformis strain BCRC11702), Bme (Bacillus methylotrophicus strain CBMB205)

and VUVD was the identified strain and the gaps are shown by dot (.)

38

1 50

VUVD .......... .......... ..GACGGACG CTGTC..... .........A

Bss .......... .......... .........C ATGGCGGCA. GCTATA...A

Bma .......... .......... ....CGAACG CTGGCGGCGT GCCTAATACA

Bvs .......... .......... ..GACGAACG CTGGCGGCGT GCCTAATACA

Bas .......... .......... ..GACGAACG CTGGCGGCGT GCCTAATACA

Bns .......... .TCCTGGCTC AGGACGAACG CTGGCGGCGT GCCTAATACA

Bsi TTTGAGTTTG ATCCTGGCTC AGGACGAACG CTGGCGGCGT GCCTAATACA

Bls .......... .......... ....CGAACG CTGGCGGCGT GCCTAATACA

Bme .......... .......... .......... .GGGNGGCNN GCTATA...A

Consensus .......... .......... ..gacg.acg cTGgCggc.. gc...a...A

51 100 100

VUVD TGCA.GTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bss TGCA.GTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bma TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bvs TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bas TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bns TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bsi TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Bls TGCAAGTCGA GCGGACCGAC GGGAGCTTGC TCCCTTAGGT CAGCGGCGGA

Bme TGCAAGTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

Consensus TGCA.GTCGA GCGGACAGAT GGGAGCTTGC TCCCTGATGT TAGCGGCGGA

101 150

VUVD CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bss CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bma CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bvs CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bas CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bns CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bsi CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bls CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Bme CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

Consensus CGGGTGAGTA ACACGTGGGT AACCTGCCTG TAAGACTGGG ATAACTCCGG

151 200

VUVD GAAACCGGGG CTAATACCGG ATGGTTGTTT GAACCGCATG GTTCAAACAT

Bss GAAACCGGGG CTAATACCGG ATGGTTGTTT GAACCGCATG GTTCAAACAT

Bma GAAACCGGGG CTAATACCGG ATGCTTGTTT GAACCGCATG GTTCAAACAT

Bvs GAAACCGGGG CTAATACCGG ATGCTTGTTT GAACCGCATG GTTCAAACAT

Bas GAAACCGGGG CTAATACCGG ATGCTTGTTT GAACCGCATG GTTCAAACAT

Bns GAAACCGGGG CTAATACCGG ATGCTTGTTT GAACCGCATG GTTCAGACAT

Bsi GAAACCGGGG CTAATACCGG ATGGTTGTTT GAACCGCATG GTTCAGACAT

Bls GAAACCGGGG CTAATACCGG ATGCTTGATT GAACCGCATG GTTCAATCAT

Bme GAAACCGGGG CTAATACCGG ATGGTTGTTT GAACCGCATG GTTCAGACAT

Consensus GAAACCGGGG CTAATACCGG ATGgTTGTTT GAACCGCATG GTTCAaACAT

201 250

VUVD AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bss AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bma AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bvs AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bas AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bns AAAAAGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bsi AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bls AAAAGGTGGC TTTTAGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Bme AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

Consensus AAAAGGTGGC TTC.GGCTAC CACTTACAGA TGGACCCGCG GCGCATTAGC

39

251 300

VUVD TAGTTGGTGA GGTAACGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bss TAGTTGGTGA GGTAACGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bma TAGTTGGTGA GGTAATGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bvs TAGTTGGTGA GGTAATGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bas TAGTTGGTGA GGTAACGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bns TAGTTGGTGA GGTAACGGCT CACCAAGGCA ACGATGCGTA GCCGACCTGA

Bsi TAGTTGGTGA GGTAACGGCT CACCAAGGCG ACGATGCGTA GCCGACCTGA

Bls TAGTTGGTGA GGTAACGGCT CACCAAGGCG ACGATGCGTA GCCGACCTGA

Bme TAGTTGGTGA GGTAACGGCT CACCAAGGCG ACGATGCGTA GCCGACCTGA

Consensus TAGTTGGTGA GGTAAcGGCT CACCAAGGCa ACGATGCGTA GCCGACCTGA

301 350

VUVD GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bss GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bma GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bvs GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bas GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bns GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bsi GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bls GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Bme GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

Consensus GAGGGTGATC GGCCACACTG GGACTGAGAC ACGGCCCAGA CTCCTACGGG

351 400

VUVD AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bss AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bma AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bvs AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bas AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bns AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bsi AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bls AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Bme AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

Consensus AGGCAGCAGT AGGGAATCTT CCGCAATGGA CGAAAGTCTG ACGGAGCAAC

401 450

VUVD GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bss GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bma GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bvs GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bas GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bns GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bsi GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Bls GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAACTCTG TTGTTAGGGA

Bme GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

Consensus GCCGCGTGAG TGATGAAGGT TTTCGGATCG TAAAGCTCTG TTGTTAGGGA

451 500

VUVD AGAACAAGTA CCGTTCGAAT AGGGCGGTAC CTTGACGGTA CCTAACCAGA

Bss AGAACAAGTA CCGTTCGAAT AGGGCGGTAC CTTGACGGTA CCTAACCAGA

Bma AGAACAAGTA CCGTTCGAAT AGGGCGGTAC CTTGACGGTA CCTAACCAGA

Bvs AGAACAAGTG CCGTTCNAAT AGGGCGGCAC CTTGACGGTA CCTAACCAGA

Bas AGAACAAGTG CCGTTCAAAT AGGGCGGCAC CTTGACGGTA CCTAACCAGA

Bns AGAACAAGTG CCGTTCAAAT AGGGCGGCAC CTTGACGGTA CCTAACCAGA

Bsi AGAACAAGTG CCGTTCAAAT AGGGCGGCAC CTTGACGGTA CCTAACCAGA

Bls AGAACAAGTA CCGTTCGAAT AGGGCGGTAC CTTGACGGTA CCTAACCAGA

Bme AGAACAAGTG CCGTTCAAAT AGGGCGGCAC CTTGACGGTA CCTAACCAGA

Consensus AGAACAAGTa CCGTTCgAAT AGGGCGGtAC CTTGACGGTA CCTAACCAGA

40

501 550

VUVD AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bss AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bma AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bvs AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bas AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bns AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bsi AAGCCACGGC TAATTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bls AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Bme AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

Consensus AAGCCACGGC TAACTACGTG CCAGCAGCCG CGGTAATACG TAGGTGGCAA

551 600

VUVD GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bss GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bma GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTCCTTAAG

Bvs GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bas GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bns GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bsi GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Bls GCGTTGTCCG GAATTATTGG GCGTAAAGCG CGCGCAGGCG GTTTCTTAAG

Bme GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

Consensus GCGTTGTCCG GAATTATTGG GCGTAAAGGG CTCGCAGGCG GTTTCTTAAG

601 650

VUVD TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bss TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bma TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bvs TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bas TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bns TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bsi TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bls TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

Bme TCTGATGTGA AAGCCCCCGG CTCAACCCGG GGAGGGTCAT TGGAAACTGG

Consensus TCTGATGTGA AAGCCCCCGG CTCAACC.GG GGAGGGTCAT TGGAAACTGG

651 700

VUVD GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bss GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bma GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bvs GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bas GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bns GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TAC.ACGTGT AGCGGTGAAA

Bsi GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bls GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Bme GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

Consensus GGAACTTGAG TGCAGAAGAG GAGAGTGGAA TTCCACGTGT AGCGGTGAAA

701 750

VUVD TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bss TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bma TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bvs TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bas TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bns TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bsi TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bls TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Bme TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

Consensus TGCGTAGAGA TGTGGAGGAA CACCAGTGGC GAAGGCGACT CTCTGGTCTG

41

751 800

VUVD TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bss TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bma TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bvs TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bas TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bns TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bsi TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bls TAACTGACGC TGAGGCGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Bme TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

Consensus TAACTGACGC TGAGGAGCGA AAGCGTGGGG AGCGAACAGG ATTAGATACC

801 850

VUVD CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bss CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bma CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bvs CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bas CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bns CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bsi CTGGTATTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Bls CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG AGGGTTTCCG

Bme CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

Consensus CTGGTAGTCC ACGCCGTAAA CGATGAGTGC TAAGTGTTAG GGGGTTTCCG

851 900

VUVD CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bss CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bma CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bvs CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bas CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bns CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bsi CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGT

Bls CCCTTTAGTG CTGCAGCAAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Bme CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

Consensus CCCCTTAGTG CTGCAGCTAA CGCATTAAGC ACTCCGCCTG GGGAGTACGG

901 950

VUVD TCGCAAGACT GAAACTCAAA GGAATTGACG GGGGGCCCGC ACAAGCGGTG

Bss TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bma TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bvs TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bas TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bns TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bsi TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bls TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Bme TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

Consensus TCGCAAGACT GAAACTCAAA GGAATTGACG GGGG.CCCGC ACAAGCGGTG

951 1000

VUVD GAGCATGTGG TTTAATTCGA AGCAACGCGA AGGAACCCTT ACCAGGGTCT

Bss GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bma GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bvs GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bas GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bns GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bsi GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bls GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Bme GAGCATGTGG TTTAATTCGA AGCAACGCGA AGAA..CCTT ACCAGG....

Consensus GAGCATGTGG TTTAATTCGA AGCAACGCGA AGaA..CCTT ACCAGG....

42

1001 1050

VUVD TTGACAGGTC TTGACATCCT CTGACAATCC TAGAGATAG. ..........

Bss ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Bma ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Bvs ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Bas ........TC TTGACATCCT CTGACANCCC TAGAGATAGG GCTTCCCCTT

Bns ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Bsi ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Bls ........TC TTGACATCCT CTGACAACCC TAGAGATAGG GCTTCCCCTT

Bme ........TC TTGACATCCT CTGACAATCC TAGAGATAGG ACGTCCCCTT

Consensus ........TC TTGACATCCT CTGACAAtCC TAGAGATAGg acgtcccctt

1051 1100

VUVD .......... .......... .......... .......... ..........

Bss CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bma CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bvs CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bas CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bns CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bsi CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bls CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Bme CGGGGGCAGA GTGACAGGTG GTGCATGGTT GTCGTCAGCT CGTGTCGTGA

Consensus cgggggcaga gtgacaggtg gtgcatggtt gtcgtcagct cgtgtcgtga

1101 1150

VUVD .......... .......... .......... .......... ..........

Bss GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bma GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bvs GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bas GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bns GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bsi GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bls GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Bme GATGTTGGGT TAAGTCCCGC AACGAGCGCA ACCCTTGATC TTAGTTGCCA

Consensus gatgttgggt taagtcccgc aacgagcgca acccttgatc ttagttgcca

1151 1200

VUVD .......... .......... .......... .......... ..........

Bss GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bma GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bvs GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bas GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bns GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bsi GCATTCAGTT GGGCCACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bls GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Bme GCATTCAGTT GGGC.ACTCT AAGGTGACTG CCGGTGACAA ACCGGAGGAA

Consensus gcattcagtt gggc.actct aaggtgactg ccggtgacaa accggaggaa

1201 1250

VUVD .......... .......... .......... .......... ..........

Bss GGT....... .......... .......... .......... ..........

Bma GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bvs GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bas GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bns GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bsi GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bls GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Bme GGTGGGGATG ACGTCAAATC ATCATGCCCC TTATGACCTG GGCTACACAC

Consensus ggt....... .......... .......... .......... ..........

43

1251 1300

VUVD .......... .......... .......... .......... ..........

Bss .......... .......... .......... .......... ..........

Bma GTGCTACAAT GGACAGAACA AAGGGCAGCG AAACCGCGAG GTTAAGCCAA

Bvs GTGCTACAAT GGACAGAACA AAGGGCAGCG AAACCGCGAG GTTAAGCCAA

Bas GTGCTACAAT GGACAGAACA AAGGGCAGCG AGACCGCGAG GTTAAGCCAA

Bns GTGCTACAAT GGNCAGAACA AAGGGCAGCG AAACCGCGAG GTTAAGCCAA

Bsi GTGCTACAAT GGGCAGAACA AAGGGCAGCG AAACCGCGAG GTTAAGCCAA

Bls GTGCTACAAT GGGCAGAACA AAGGGCAGCG AAGCCGCGAG GCTAAGCCAA

Bme GTGCTACAAT GGACAGAACA AAGGGCAGCG AAACCGCGAG GTTAAGCCAA

Consensus .......... .......... .......... .......... ..........

1301 1350

VUVD .......... .......... .......... .......... ..........

Bss .......... .......... .......... .......... ..........

Bma TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bvs TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bas TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bns TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bsi TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bls TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Bme TCCCACAAAT CTGTTCTCAG TTCGGATCGC AGTCTGCAAC TCGACTGCGT

Consensus .......... .......... .......... .......... ..........

1351 1400

VUVD .......... .......... .......... .......... ..........

Bss .......... .......... .......... .......... ..........

Bma GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bvs GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bas GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bns GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bsi GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bls GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Bme GAAGCTGGAA TCGCTAGTAA TCGCGGATCA GCATGCCGCG GTGAATACGT

Consensus .......... .......... .......... .......... ..........

1401 1450

VUVD .......... .......... .......... .......... ..........

Bss .......... .......... .......... .......... ..........

Bma TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bvs TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bas TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bns TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bsi TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bls TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Bme TCCCGGGCCT TGTACACACC GCCCGTCACA CCACGAGAGT TTGTAACACC

Consensus .......... .......... .......... .......... ..........

1451 1500

VUVD .......... .......... .......... .......... ..........

Bss .......... .......... .......... .......... ..........

Bma CGAAGTCGGT GAGGTAACCT TTATGGAGCC AGCCGCCGAA GGTGGGAC.A

Bvs CGAAGTCGGT GAGGTAACCT TTTAGGAGCC AGCCGCCGAA GGTGGGAC.A

Bas CGAAGTCGGT GAGGTAACCT TTATGGAGCC AGCCGCCGAA GGTGGGAC.A

Bns CGAAGTCGGT GAGGTAACCT TTTAGGAGCC AGCCGCCGAA GGTGGGAC.A

Bsi CGAAGTCGGT GAGGTAACCT TTATGGAGCC AGCCGCCGAA GGTGGGACGA

Bls CGAAGTCGGT GAGGTAACCT TT.TGGAGCC AGCCGCCGAA GGTGGGAC.A

Bme CGAAGTCGGT GAGGTAACCT TTTAGGAGCC AGCCGCCGAA GGTGAC....

Consensus .......... .......... .......... .......... ..........

44

1501 1543

VUVD .......... .......... .......... .......... ...

Bss .......... .......... .......... .......... ...

Bma GATGATTGGG G......... .......... .......... ..

Bvs GATGATTGGG GTGAAG.... .......... .......... ...

Bas GATGATTGGG GTGAAG.... .......... .......... ...

Bns GATGATTGGG GTGAAGTC.G TAACAAGGTA GCCGTATCGG AAG

Bsi GATGATTGGG GTGAATGCAG TAACAAGGTA GCCG.ATCGA TGC

Bls GATGATTGGG G......... .......... .......... ...

Bme .......... .......... .......... .......... ...

Consensus .......... .......... .......... .......... ...

Fig. 3.15 Multiple sequence alignment of 16S rDNA nucleotides from species related

to Bacillus using Multalin software

3.7.3 Cluster analysis

The bacterial species could be recognized based on similarity in the clusters of

genotypes. The researchers used MSA approach, to find the relationships among the

closely related species within a single genus (Christensen et al. 2004; Hanage et al,

2006). The sequence clustering of species was done by Multalin software version

5.4.1 and this study revealed that the VUVD (VUVD001 isolate) was highly similar to

the Bss (B.subtilis starin DBT13) (Fig. 3.16)

Fig. 3.16 Cluster sequencing for VUVD001 by Multalin software

3.7.4 Phylogenetic tree construction

The evolutionary analysis was conducted using MEGA6. The evolutionary history

was inferred by using the Maximum Likelihood method based on the Kimura 2-

parameter model. The bootstrap consensus tree inferred from 1000 replicates is taken

to represent the evolutionary history of taxa analyzed. The results of phylogenetic tree

showed that the strain VUVD001 was 77% similar to Bacillus subtilis strain DBT13

from GenBank database (Fig.3.17).

45

Bacillus methylotrophicus strain CBMB205

Bacillus siamensis strain PD-A10

Bacillus nematocida strain B-16

Bacillus vallismortis strain NBRC 101236

Bacillus atrophaeus strain NBRC 15539

VUVD 001

Bacillus subtilis strain DBT13

Bacillus malacitensis strain LMG 22477

Bacillus licheniformis strain BCRC 11702

77

51

35

15

30

25

Fig. 3.17 Dendrogram constructed by maximum-likelihood method using MEGA 5v

The 16S partial sequence of VUVD001 isolate was submitted to the National Center

for Biotechnology Information (NCBI) and accepted as new strain of Bacillus subtilis

VUVD0001 strain with the accession number of KT894158 (Fig. 3.18).

Fig. 3.18 The 16S partial sequence of B.subtilis VUVD001 strain to NCBI

46

3.8 SUMMARY

The lactase producing potential bacterial strain was isolated from dairy effluent and

named as VUVD001. Based on morphological and biochemical studies the VUVD001

confirmed that it belongs to genus of Bacillus. To further confirm the particular strain

the VUVD001 isolate was subjected to 16S rRNA analysis. From this study the strain

was identified as Bacillus subtilis VUVD001 strain which is 77% closer to the

B.subtilis strain DBT13. The antimicrobial activity of VUVD001 isolate has shown

the highest inhibition activity on Enterobacter aerogenes and Klebsiella pneumonia.

Prior to the production the growth proliferation ability of B.subtilis VUVD001 strain

was studied by growth curve experiment.

47

CHAPTER-IV

OPTIMIZATION OF NUTRITIONAL COMPONENTS OF THE

MEDIUM FOR THE ENHANCED LACTASE PRODUCTION

4.1 INTRODUCTION

The production medium cost and low productivity of the enzymes were the main

problem in commercial scale production. The production level was highly influenced

by nutritional composition of medium particularly, carbon and nitrogen sources.

Hence, the design of suitable medium with lower cost and higher lactase activity was

highly essential (Tari et al, 2007). The traditional optimization method (OFAT)

involves varying one variable at a time and other factors were maintained at a fixed

level. However, OFAT is time consuming and effect of interaction of multiple factors

on production was unable study in a single experiment. To overcome this, Response

Surface Methodology (RSM) has been employed in the optimization for production of

lactase. The RSM is a collection of statistical tools used for experimental design and

evaluating the effects of variables and identifying the most desirable conditions for

enhanced production of lactase (Siham and Hashem, 2009; Charyulu and Gnanamani).

Hsu et al, (2005) was investigated the effect of medium components on lactase

production by RSM and they found the maximum lactase production by the

cultivation of Bifidobacteria. Liu et al (2013) was applied the RSM to investigate the

impact of process factors such as wheat bran, soybean meal, KH2PO4, MnSO4·H2O

and CuSO4·5H2O on lactase production from Aspergillus foetidus. Abdul Khalil et al,

(2014) was used the RSM tool for the optimization of milk based medium. They were

reported that the yeast extract is a potential nitrogen source for enhanced production

of lactase through the cultivation of Bifidobacterium pseudocatenulatum G4. Liu et al

(2007) was applied Response Surface Methodology for improved production of

lactase by assess the impact of factors such as concentrations of wheat bran, soybean

meal, KH2PO4, MnSO4.H2O and CuSO4.5H2O on production from the microbial

species. Adikane et al (2015) examined the Pantoea sp. for enhanced production of

lactase through the optimization of milk whey medium by RSM.

48

4.2 MATERIALS AND METHODS

4.2.1 Microorganism and Shake flask fermentation

The organism used in this study was B.subtilis strain VUVD001. The culture was

maintained in our laboratory at room temperature and preserved at 4 o

C on nutrient

agar medium. The original fermentation medium consisted of 4 g/l of Lactose, 4 g/l of

Yeast extract, 1 g/l of MgSO4·7H2O and 0.1g/l of Tryptophan. The shake-flask

fermentation was carried out by inoculating 5 ml seed culture in a conical flask

containing 100 ml of production medium. Then, the flasks were incubated at 36 °C on

a rotary shaker (180 rpm) for 36 h.

4.2.2 Effect of various nutrients sources

The suitable carbon and nitrogen sources were identified for the production of enzyme

by allowing bacterial strain proliferation in production medium. The medium was

supplemented with different carbon sources such as lactose, glucose, sucrose, fructose

and starch in concentration range of 0.4% and 0.4% of nitrogen sources like yeast

extract, urea, sodium nitrate, ammonium nitrate were added to the culture medium to

investigate their effect on enzyme production. Similarly, the metal ions (0.1%),

MgSO4, MnSO4 and ZnSO4 and 0.01% of different amino acids namely glycine,

tryptophan and cysteine were added to the medium to investigate its significant effect

on production (Sharma and Singh, 2014).

4.2.3 RSM for optimization of Medium

The Design Expert software (Version 7.0.0, Stat-Ease Inc., Minneapolis, USA), was

used to optimize the production medium variables namely Lactose (A), Yeast extract

(B), Tryptophan (C) and MgSO4 (D) are shown in Table 4.1. The range of variables

from low (-1) to high (+1) were used to study the effect of independent variables on

production. Regression analysis of experimental data and response surface plots were

performed. The model was validated by conducting experiment at given optimal

variables of the medium.

Table 4.1 Variable ranges used in experimental design

Symbol Name of the Variable Range

A Lactose, g/l 5 – 20

B Yeast extract, g/l 5 - 15

C Tryptophan, g/l 0.2 – 0.6

D MgSO4, g/l 2 – 6

49

4.3 RESULTS AND DISCUSSION

4.3.1 Optimization of nutritional components of medium by one-factor-at-a-time

method

4.3.2 Effect of carbon sources on enzyme production

The amount of carbon source in fermentation media is primary energy source which is

essential for bacterial growth and production of lactase in submerged fermentation.

Carbon source may regulate the biosynthesis of lactase in different microorganisms

(Alazzeh et al, 2009). The fermentation medium was supplemented with 0.4% of

different carbon sources to study their individual influence on production by

traditional optimization. The molecules like glucose and fructose were also showed

the increase of biomass and yield but they were less efficient as compared with lactose

(15.14 U/ml) (Fig.4.1a). Further the lactose quantity was optimized by varying the

concentration from 0.5 to 2% and found that significant improvement in lactase

activity (24.84 U/ml) and biomass with the addition of 1.5% of lactose (Fig.4.1b).

Sriphannam et al (2012) has reported lactose may enhance the production of lactase

by probiotic strain Lactobacillus fermentum CM33 in submerged process. In previous

study, the strain was reported for improved activity of lactase by using lactose as sole

carbon source from a Thalassospira frigidphilosprofundus through submerged

fermentation and hence our results were supported by the previous reports (Pulicherla

et al, 2013).

Fig.4.1 Effect of various carbon sources on Lactase production and Biomass. a)

Different carbon sources of glucose, lactose, fructose, starch and sucrose and b)

different concentrations of lactose in %

50

4.3.3 Effect of nitrogen source

The nitrogen was an important factor which may affect microbial biosynthesis of

lactase (Shaikh et al, 1997). The effect of different nitrogen sources namely sodium

nitrate, ammonium nitrate, yeast extract and urea on cell growth and lactase

production were investigated. In addition, yeast extract and ammonium nitrate were

showed the enhancement in lactase activity but the effectiveness of ammonium nitrate

was less as compared with yeast extract (Fig. 4.2a). Further, the concentration of yeast

extract for production was evaluated simultaneously by varying the concentration and

the maximum lactase activity of 22.19 U/ml was obtained at one % of yeast extract

(Fig. 4.2b). The similar study was also conducted by Hsu et al (2007) using

Bifidobacterium longum CCRC15708 in shake flask culture system. He reported

highest lactase was produced with yeast extract as nitrogen source. Similarly Mukesh

Kumar et al (2012) also reported the activity of lactase was improved with the

supplementation of 1% of yeast extract in the submerged fermentation with Bacillus

sp. MNJ23.

Fig.4.2 Effect of nitrogen source on Lactase production and Biomass. a)

Different sources i.e., Yeast extract, sodium nitrate, ammonium nitrate and urea

and b) Yeast extract concentrations in %.

4.3.4 Effect of tryptophan on lactase activity

The amino acid role in medium was described as stimulator for biosynthesis and

excretion of enzymes (Gupta et al, 2003). Therefore, production medium with amino

acid may consider as experimental tool for significant enhancement of enzyme

production. Based on this observation the amino acids tryptophan, glycine and

cysteine, were selected to study its impact on biomass and lactase production. The

51

results indicated that activity was improved with addition of tryptophan. Among the

three amino acids tryptophan resulted the highest activity and the corresponding report

were represented in Fig.4.3a. Therefore, the effect of tryptophan concentration on

production was further evaluated for improving the production rate. However the

medium feeding with 0.04% tryptophan was significantly enhanced the enzyme

production. Under this condition, the production had reached 25.10 U/ml in the shake-

flask fermentation (Fig.4.3b). Akcan (2011) reported the addition of L-tryptophan

may enhace the biosynyhesis of lactase by Bacillus licheniformis ATCC12759 in

submerged fermentation.

Fig.4.3 Effect of amino acids on Lactase production and Biomass. a) Different amino

acids i.e., Glycine, L-Tryptophan and L-Cysteine and b) Tryptophan concentrations in

%

4.3.5 Effect of metal sources on Lactase activity

The effect of metal ions on biosynthesis of enzyme production was screened by

supplementing medium with 0.1% of metal sources like MgSO4, MnSO4 and ZnSO4.

The enzyme activity was improved with MgSO4 compared to MnSO4 and ZnSO4 and

the results were displayed in Fig.4.4a. In addition the impact of MgSO4 was further

investigated at different concentration ranges. Among three ionic compounds, MgSO4

enhanced the production with a highest activity of 22.12 U/ml at 0.4%. (Fig.4.4b). The

contribution of this metal ion as an ion channeling agent in membrane

permeabilization has been reported (Karpen and Ruiz, 2002). Previous report revealed

that the production of lactase was significantly improved with the supplementation of

10 mg of MgSO4 per gram of substrate (Gopal et al, 2015). The MnSO4 also showed

the considerable effect on the production of lactase but it was less efficient than

52

MgSO4. In previous report addition of 1 mM of Mn2+

to the medium was showed the

significant improvement in lactase activity through L.crispatus fermentation (Kim and

Rajagopal, 2000). Previous report showed a slight stimulatory influence on lactase

activity by addition of MnCl2 with concentration of 0.02 M in shake flask culture with

L.acidophilus NRRL4495. Based on the experimental results it was found that the

addition of divalent ion sources such as Mg2+

and Mn2+

to growth medium may

enhance the yield.

Fig.4.4 Effect of minerals on Lactase production and Biomass. a) Different

minerals i.e., MgSO4, ZnSO4 and MnSO4 and b) MgSO4 concentrations in %

4.3.6 Optimization of nutritional components of medium by Central Composite

Design (CCD)

4.3.6.1 CCD for medium optimization

After one factor at a time optimization study, activity of lactase was significantly

improved as compared with basal medium. For further enhancing the lactase yield, the

Central Composite Design (CCD) was adapted for design of optimum concentration

of nutrient components such as lactose, yeast extract tryptophan and MgSO4. The

experimental results of CCD experimental designs for production were shown in

Table 4.2.

53

Table 4.2 The design data of CCD and lactase activity values

Run

No

Lactose

(g/l)

Yeast

extract

(g/l)

Tryptophan

(g/l)

MgSO4

(g/l)

Activity, U/ml

Experimental Predicted

1 5 5 0.6 6 28.31±0.007 26.17

2 20 5 0.6 6 27.46±0.011 29.10

3 5 5 0.2 2 25.18 ±0.009 24.11

4 5 5 0.6 2 28.75±0.005 30.48

5 12.5 10 0.4 4 63.04±0.013 60.70

6 5 15 0.2 6 34.25 ±0.006 34.73

7 5 15 0.6 6 41.09 ±0.004 41.76

8 12.5 10 0.4 2 61.74 ±0.010 61.87

9 5 5 0.2 6 27.41 ±0.008 27.06

10 20 10 0.4 4 51.62 ±0.007 52.46

11 12.5 5 0.4 4 38.75±0.012 42.04

12 12.5 10 0.4 4 62.84±0.002 60.70

13 12.5 10 0.4 4 61.02±0.014 60.70

14 5 15 0.2 2 23.14 ±0.005 22.39

15 12.5 10 0.4 4 60.54 ±0.013 60.70

16 20 5 0.2 6 37.91 ±0.009 36.57

17 20 5 0.6 2 35.75 ±0.005 33.77

18 5 15 0.6 2 36.85 ±0.011 36.69

19 20 15 0.2 6 57.12 ±0.006 56.28

20 12.5 15 0.4 4 55.85 ±0.010 54.99

21 20 15 0.6 6 57.16 ±0.003 56.73

22 12.5 10 0.6 4 62.47 ±0.016 61.89

23 20 15 0.6 2 50.76 ±0.013 52.00

24 20 15 0.2 2 43.65 ±0.009 44.29

25 20 5 0.2 2 33.75 ±0.023 33.97

26 12.5 10 0.4 6 63.42 ±0.012 65.71

27 5 10 0.4 4 38.45 ±0.009 40.04

28 12.5 10 0.2 4 55.48 ±0.003 58.48

29 12.5 10 0.4 4 63.01 ±0.014 60.70

30 12.5 10 0.4 4 61.05 ±0.008 60.70

The response, Y was fitted with the second-order polynomial equation.

Lactase activity (U/ml), R1 = +60.70 +6.21*A+6.48 * B+1.71* C+1.92 * D+3.01 *A*

B-1.64*A*C-0.087*A*D+1.98 *B* C+2.35*B*D-1.82*C*D-14.46*A2-12.19* B2-

0.52* C2+3.09 * D2

The model equation importance was statistically evaluated by the F test for the

analysis of variance (ANOVA). The prob > F values (<0.0001) for the Lactase

54

production are lower than 0.05, indicating that goodness of designed quadratic model

was significant. Based on observation from ANOVA table it was found that the

variables A, B, C, D, AB, AC, BC, BD, CD, A2, B2 and D2 are significant model

terms. The coefficient (R2) value was found to be 0.98 and this value also supported a

high correlation between experimental and predicted values. The lower consistency of

the experiment is generally indicated by high value of coefficient of variation (CV). In

this case, low % CV (4.55) represents that the experiment performed is reliable.

Adequate precision ratio greater than 4.00 is desirable. In present study, the ratio is

29.117 which indicate an adequate signal (Table 4.3).

Table 4.3 ANOVA analysis of model and medium components

Source Sum of

Squares df

Mean

Square F Value

p-value

Prob > F

Model 5707.57 14 407.684 92.0795 < 0.0001

A-Lactose 693.781 1 693.781 156.698 < 0.0001

B-Yeast extract 755.309 1 755.309 170.594 < 0.0001

C-Tryptophan 52.3947 1 52.3947 11.8339 0.0036

D-MgSO4 66.3552 1 66.3552 14.987 0.0015

AB 144.841 1 144.841 32.7138 < 0.0001

AC 43.2964 1 43.2964 9.77893 0.0069

AD 0.1225 1 0.1225 0.02767 0.8701

BC 62.7264 1 62.7264 14.1674 0.0019

BD 88.1721 1 88.1721 19.9146 0.0005

CD 52.7802 1 52.7802 11.9209 0.0036

A2 541.389 1 541.389 122.278 < 0.0001

B2 385.021 1 385.021 86.9609 < 0.0001

C2 0.68811 1 0.68811 0.15542 0.699

D2 24.7326 1 24.7326 5.58612 0.032

Residual 66.4128 15 4.42752

Lack of Fit 59.6527 10 5.96527 4.41209 0.0575

Pure Error 6.76013 5 1.35203

Cor Total 5773.99 29

R-Squared 0.98 Adeq Precision 29.117 C.V. % 4.55

55

The 3-D response surface curves were plotted to evaluate interactions of

combinational medium variables effects on response and to find optimal

concentrations of nutrient sources for lactase production (Fig. 4.5 to 4.10).

Fig.4.5 Effect of yeast extract and lactose on lactase activity

56

Fig. 4.6 Effect of tryptophan and lactose on lactase activity

Fig. 4.7 Effect of MgSO4 and lactose on lactase activity

57

Fig. 4.8 Effect of tryptophan and yeast extract on lactase activity

Fig. 4.9 Effect of MgSO4 and yeast extract on lactase activity

58

Fig.4.10 Effect of MgSO4 and tryptophan on lactase activity

4.3.6.2 Validation of RSM Model

The RSM model is validated by conducting an experiment at best predicted solution

for production of lactase. Under optimized conditions the enzyme activity was reached

to 63.54 U/ml from Bacillus subtilis VUVD001 this value is almost near to the RSM

predicted value (Table 4.4).

Table 4.4 RSM Optimized medium variables for lactase activity

A-

Lactose

(g/l)

B-

Yeast

extract

(g/l)

C-

Tryptophan

(g/l)

D-

MgSO4

(g/l)

Activity, U/ml

Predicted Experimental

14.01 10.30 0.43 5.32 64.49 63.54±0.021

Previously, reported the maximum lactase activity of 18.6 U/ml was obtained in

submerged fermentation with Bifidobacterium longum CCRC 15708. The process was

run at optimum conditions of pH 6.5, temperature 37 oC and incubation time 16 h in

liquid medium contains 4% lactose, 3.5% yeast extract, 0.3% K2HPO4, 0.1%

59

KH2PO4, 0.05% MgSO4.7H2O and 0.03% L-cysteine (Hsu et al, 2007). Similarly

Prasad et al (2013) reported that the highest intracellular lactase activity of 6.80 U/ml

and 7.7 U/ml were observed by cultivation of Bifidobacterium animalis spp. lactis

Bb12 and Lactobacillus delbrueckii spp. bulgaricus ATCC 11842 on whey. Both

microbes were grown in protein free whey medium containing yeast extract (3.0 g/l),

peptone (5.0 g/l) and glucose (10.0 g/l) for 18 h, at 37 ºC for B. animalis ssp. lactis

Bb12 and at 45 ºC for L. delbrueckii ssp. bulgaricus ATCC11842. Compared to the

Prasad et al (2013) report our strain is an extracellular producer and the lactase

activity is increased around 9.3 and 8.4 folds. In the fermentation process they used

multiple nitrogen sources which increase the overall cost of the process but in our

study we used the single nitrogen sources which may reduce cost of process.

4.4 SUMMARY

The production was improved through optimization methods like OFAT and RSM, in

OFAT method first the mineral sources MgSO4 was optimized and then followed by

yeast extract, lactose and tryptophan. The activity was improved from 15.27 U/ml to

25.10 U/ml through the optimization of nutrient components by OFAT method. The

production was further improved from 25.10 U/ml to 63.54 U/ml through the

statistical optimization of the same nutritional components by Response Surface

Methodology. The production was 2.5 folds superior when compared to traditional

optimization.

60

61

CHAPTER-V

MODELING AND OPTIMIZATION OF PHYSICAL VARIABLES BY

ARTIFICIAL NEURAL NETWORKS AND RESPONSE SURFACE

METHODOLOGY

5.1 INTRODUCTION

The physical variables like pH, temperature and incubation time, aeration and

agitation are the important factors and have vigilant effect on metabolic activities of

microorganisms (Al-Jazairi et al, 2015). Modeling and optimization are main stages in

biotechnology manufacturing sector to enhance the effectiveness of fermentation

process and lowering the cost of process. The classical optimization method is not

only time-consuming and it will not predict the complete effects of the variables in the

process and also ignores combined interactions between the parameters (Baş and

Boyaci, 2007). Meanwhile, the Response Surface Methodology (RSM) is an empirical

modeling algorithm for maximizing and optimizing the various factors. RSM assesses

-the relationships between independent variables and defines its effect on process

(Chen et al, 2002). Many researchers were successfully applied the RSM for

production of industrially important enzymes from various microorganisms (Basri et

al, 2007). Tari et al (2009) was studied the production of lactase from the

Streptococcus thermophilus 95/2 (St 95/2) and Lactobacillus delbrueckii ssp.

bulgaricus 77 (Lb 77) through optimization of process variables by RSM. Dagbaglı et

al (2009) applied the RSM for optimization of physical parameters such as aeration

rate, agitation speed, incubation time for production of lactase from yeast species. The

modeling techniques namely artificial intelligence and evolutionary computing were

developed based on the phenomenon of applied biological systems. In the present

scenario, Artificial Neural Networks (ANN) are the most popular learning technique

in the field of biotechnology for optimization of various factors in the enzymes

production from microbes (Dutta et al, 2004; Manohar et al, 2005). In previous studies

Afshin et al (2008) was used the ANN for modeling of bioprocess parameters in the

production of enzyme from Geobacillus sp. strain ARM. Rao et al (2008) carried out

their research work on modeling and optimization of process parameters for improved

production of enzyme from B. circulans by ANN and genetic algorithm (GA).

62

5.2 MATERIALS AND METHODS

5.2.1 Optimization of physical factors by OFAT

The medium consists of (in g/l); lactose, 4; yeast extract, 4; MgSO4·7H2O, 1 and

tryptophan, 0.1 was used for optimization study by OFAT. The physical factors of the

process were optimized by conducting the experiments at different temperatures; 27

ºC, 37 oC and 47 ºC and different pH values ranges from 5 to 8.0. The isolate was also

tested for inoculums size from 1% to 6% and incubation time at different intervals 12

h, 24 h, 36 h and 42 h.

5.3 Optimization of physical variables by RSM

5.3.1 Medium and shake flask experiment

One loop full bacterial culture of B. subtilis VUVD001 from agar slant of 36 h old

was transferred to lactose broth medium for inoculum preparation and 5 ml of

inoculums was transferred to the 100 ml of production medium. The compositions of

medium were: (in g/l) lactose 14.01, yeast extract 10.30, tryptophan 0.43 and MgSO4

5.32l.

5.3.2 Culture conditions

The liquid fermentation was conducted batch wise in 250 ml flasks using the lactose

broth medium and sterilized at 121 oC for 15 min. The flasks were inoculated with 5

% inoculum and incubated at 37 oC on rotary shaker at 150 rpm. The growth condition

levels of Temperature, pH and incubation time used in the optimization study by an

application of Response Surface Methodology are given in Table 5.1.

Table 5.1 Physical variable ranges used in experimental design

Symbol Name of the Variable Range

A Temperature,oC 35 – 40

B pH 5 – 8

C Incubation time, h 10 – 50

5.3.3 Prediction of variables effect on production

Box-Behnken method was applied to predict maximum ranges of the significant

factors (temperature, pH and incubation time) and their combinational interaction on

production. Design Expert version 7.0 software was utilized for design of experiment,

data analysis and development of statistical model. Each experiment was studied in

triplicate and average yield obtained was taken as the response (R), while the

predicted values of response were obtained from quadratic model. The variables

63

impact on response was predicted through regression analysis of data. The three

dimensional surface plots was obtained to understand the variable effect and to

determine their optimum levels for production. Response surface model was fit to

response i.e. activity of lactase (U/ml). The second order response function for three

variables is given by the following equation.

Where R is dependent variable (lactase production) and A, B and C are independent

variables (temperature, pH and incubation time respectively), β0 is an intercept term,

β1, β2 and β3 are linear coefficients, β12, β13 and β23 are the interaction coefficients, β11,

β22 and β33 are the quadratic coefficients.

5.4 Modeling using Artificial Neural Networks (ANN)

ANN modeling is an alternative tool to RSM for regression analysis of polynomial

non-linear systems. ANN architecture is an interlinked complex with elements like

neurons and the connections between the neurons were described by weights (w) and

bias (b). The neurons were controlled by transfer and summing functions and general

transfer functions includes purelin, log sig and tan sig (Das et al, 2015). In the present

study, the predictive model was developed using temperature (oC), pH and incubation

time (h) as input variables and lactase activity (U/ml) as the output for the model. The

input layer function is to pass the scaled input values to hidden layer through weights.

A back-propagation algorithm is used with one hidden layer enhanced with

Levenberg-Marquardt optimization method (Arcaklioglu et al, 2004).

5.5 RESULTS AND DISCUSSION

5.5.1 Effect of incubation time on production

The changes in enzyme activity were observed during incubation periods from 12 to

48 h. The maximum enzyme activity of 15.13 U/ml was found at 36 h of incubation

beyond this the activity was declined due to depletion of nutrients (Fig. 5.1a).

Similarly, Qian et al (2013) reported that highest lactase activity was found at 36 h of

incubation time through fermentation in shake flasks with thermotolerant strain.

Mukesh Kumar et al (2012) and Jayashree Natarajan et al (2012) were also found

optimum lactase activity at 48 h of incubation period in submerged fermentation

process by using Bacillus sp. MPTK121 and Bacillus sp. correspondingly.

5.5.2 Effect temperature on production

The submerged fermentation process from B. subtilis VUVD001 strain was shown the

maximum enzyme activity at 37 °C and then enzyme activity slowed down beyond

64

this temperature. Thus, 37 °C was found to be optimum for lactase production by B.

subtilis strain VUVD001 (Fig.5.1b). Tryland and Fiksdal (1998) were reported that 35

°C is the maximum temperature for lactase activity and beyond this temperature the

enzyme activity slowly reduce up to 44 °C. Murad et al (2011) stated that lactase

production was increased by lactobacilli strain when the cultivation temperature is

maintained at 30 °C.

5.5.3 Effect of inoculum size on production

Inoculum size of bacterial culture has an important effect on production of maximum

quantity of enzyme. The maximum lactase activity of 15.20 U/ml was observed with

inoculum size of 5% and minimal enzyme activity was found with concentration of

1% inoculum respectively (Fig.5.1c). Gowdhami et al (2014) was observed highest

lactase production by L. bifermentans at 2% v/v inoculums.

5.5.4 Effect of pH on enzyme production

The effect pH on enzyme activity revealed that activity gradually increased from pH

5.0 to 7.0 and beyond this the activity slowly decreased with increase in pH values.

The highest enzyme activity was found to be 15.04 U/ml at pH 7.0 (Fig.5.1d).

Cherabarti et al (2003) proved that relative activity of lactase was higher at pH 7

through submerged fermentation using B.poymyxa. Prasad et al (2013) was found the

optimum intracellular lactase activity at pH 6.8 through cultivation of L.

delbrueckiispp.bulgaricus ATCC11842 and B. animalis spp.lactis Bp12.

65

Fig.5.1 Optimization of physical parameters for production of lactase; a) Temperature,

oC b) pH c) Incubation time, h and d) Inoculum size, %

5.6 Optimization of physical variables by Box-Behnken Design

Statistical model for experimental design is an essential tool in optimizing conditions

that may bring several fold increase in production. The effect of parameters and their

interaction on lactase synthesis was determined by conducting 17 experiments given

by the model. Box-Behnken design provides necessary information about effects of

variables on response. The actual data given by the model was showed in Table 5.2.

66

Table 5.2 Actual data for design of Experiments

Run

Temp.(

oC)

pH

Incubation

Time,

(h)

Activity, (U/ml)

Experimental RSM

Predicted

ANN

Predicted

1 37.5 6.5 30 88.64±0.007 86.67 85.59

2 35 8 30 67.35±0.018 61.25 67.35

3 37.5 5 50 41.26±0.011 45.07 43.13

4 35 6.5 10 28.46 ±0.018 38.37 28.46

5 40 6.5 10 9.57 ±0.012 13.66 12.79

6 37.5 5 10 30.16±0.006 19.96 30.16

7 37.5 8 50 46.53 ±0.003 56.72 44.03

8 37.5 6.5 30 81.45±0.002 86.67 85.59

9 40 6.5 50 43.44±0.015 33.52 41.98

10 37.5 6.5 30 91.04±0.014 86.67 85.59

11 37.5 6.5 30 80.12±0.017 86.67 85.59

12 40 5 30 13.55±0.008 19.64 13.55

13 40 8 30 15.65 ±0.015 15.37 15.69

14 35 5 30 23.15 ±0.009 23.42 23.15

15 37.5 8 10 45.68±0.010 41.86 45.68

16 37.5 6.5 30 92.14±0.015 86.67 85.59

17 35 6.5 50 62.57±0.001 58.47 62.57

The quadratic model equation was obtained by Box-Behnken Design, which predicts

the variables impact on response.

R1 = +86.68 -12.42 * A +8.39 * B +9.99 * C -10.52 * A * B- 0.060 * A * C - 2.56

* B * C-30.83* A2 -25.93 * B2 -19.84 * C2

Where the response (R1) indicates lactase production and other variables A, B, C

represents temperature, pH and incubation time respectively.

67

The coefficient R-squared value 0.9496 recommend the design was significant to

predict effect of variables on production by B.subtilis VUVD001. The Model F-value

of 14.64 implies the model is significant. The values of "Prob > F" less than 0.0500

indicate model terms are significant. In this case A, B, C, A2, B2, C2 are significant

model terms. Values greater than 0.1000 indicate the model terms are not significant.

The "Lack of Fit F-value" of 5.87 implies the Lack of Fit is not significant relative to

the pure error (Table 5.3).

Table 5.3 Analysis of variance of quadratic model for production of lactase

Source Sum of squares df Mean squares F-value P-value

Quadratic Model 12508.84 9 1389.871 14.63993 0.0009

A-Temperature 1233.058 1 1233.058 12.98816 0.0087

B-pH 562.6335 1 562.6335 5.926386 0.0451

C-Incubation Time 798.6006 1 798.6006 8.411898 0.0230

AB 443.1025 1 443.1025 4.667331 0.0676

AC 0.0144 1 0.0144 0.000152 0.9905

BC 26.26563 1 26.26563 0.276664 0.6151

A2 4000.825 1 4000.825 42.14189 0.0003

B2 2830.519 1 2830.519 29.8147 0.0009

C2 1657.83 1 1657.83 17.46242 0.0041

Residual 664.5592 7 94.93703

Lack of Fit 541.5099 3 180.5033 5.87 0.0602

Pure Error 123.0493 4 30.76232

Cor Total 13173.4 16

R-Squared = 0.9496, Adj. R-Squared = 0.8847, C.V. = 19.24%, Adeq. Precision = 9.770

The three dimensional response plots give understandable information to predict the

relationship between response and variable range. The graphs showed an oval shape

curve that suggests optimum conditions and combinational effects on production

(Fig.5.2 to 5.4).

68

Fig. 5.2 Effect of temperature and pH on lactase production

Fig. 5.3 Effect of temperature and incubation on the lactase production

69

Fig. 5.4 Effects of incubation time and pH on the producttion of lactase

5.6.1 Validation of RSM Model

The RSM model is validated by conducting an experiment at best predicted solution

given by RSM for production of lactase. The enzyme activity was reached to 91.32

U/ml from a B. subtilis VUVD001 after statistical optimization and this value is

almost near to the RSM predicted value (Table 5.4).

Table 5.4 RSM optimized process variables for maximum lactase activity

Temp.(oC) pH Incubation

time (h)

Activity, U/ml

A B C Predicted Experimental

36.91 6.8 34.77 90.16 91.32 ±0.014

70

It has been observed that maximum production 91.32 U/ml was attained at 36.91 oC,

pH 6.8 and incubation time of 34.77 h. In previous reports several workers achieved

highest enzyme activity at 37 oC and 24 h incubation time through submerged

fermentation with Bacillus strain B-2 (Mukesh Kumar et al, 2012), Lactobacillus

amylophilus GV6 at 48 h (Mozumder et al, 2012), Lactobacillus acidophilus (Milica

Carevic et al, 2015), Bacillus Sp.MPTK 121 (Mukesh Kumar et al, 2012) and

Lactobacillus delbrueckii (Mozumder NHMR et al, 2012). Similarly, Milica Carevic

et al in 2015 has observed optimum activity of 1.01IU ml-1

at 37 oC, pH 6.5 – 7.5 and

incubation time 48 h in shake flask culture fermentation using Lactobacillus

acidophilus ATCC 4356 (Milica Carevic et al, 2015). Earlier reported the lactase of

0.31 U/ml was achieved by Aspergillus terreus even after the RSM model (Rashmi et

al, 2011). Compared with this fungal strain our bacterium is potential for Lactase

production. Hsu et al (2007) reported the maximum beta-galactosidase activity of 18.6

U/ml was obtained in submerged fermentation with Bifidobacterium longum

CCRC15708. The process was run at optimum conditions of pH 6.5, Temperature 37

oC and Incubation time 16 h in liquid medium contain 4% lactose, 3.5% yeast extract,

0.3% K2HPO4, 0.1% KH2PO4, 0.05% MgSO4.7H2O and 0.03% L-cysteine. Anisha et

al (2008) reported the activity is reached from 17 to 50 U/ml under the conditions of

temp 33 oC and pH 7 in submerged fermentation by Streptomyces griseoloalbus in

Box-Behnken Design. Jaekoo Lee et al (2013) were gained maximum activity of

lactase is 1.06 U/ml in submerged cultivation with Bacillus sp. LX-1. In the same

way, Edupuganti et al (2014) reported that the lactase production in submerged

fermentation by Acinetobacter sp.was achievd the activity of 10.2 U/ml after the

modeling and genetic algorithm optimization. The process was done under the

optimum conditions of temperature 37 °C, pH 7.2 and agitation speed 183 rpm.

Similarly Arekal et al (2014) reported that highest lactase activity of 10.6 U/ml was

obtained with Lactobacillus plantarum MTCC5422 in the soy whey based medium at

RSM optimized conditions of incubation time 33 h, temperature 37 °C and pH 6.6.

Based these observations it was proved that the predicted model found to be

significant and the optimized process conditions were used in bioindistries for large

scale production.

71

5.7 Development of Neural Network Model and Result Analysis

Training, Testing and validation of neural network were performed with three input

variables and one output by using tools namely feed forward back propagation

network and TRAINLM in MATLAB R2013a version. The elevated regression value

of 0.9945 was attained from ANN model. The performance curve was developed

using MATLAB R2013a for training, testing and validation of data. The regression

plot of output versus target was developed with ten hidden nodes and R-squared value

0.99 was accomplishing the validation of model (Fig.5.5).

20 40 60 80

20

30

40

50

60

70

80

90

Target

Ou

tpu

t ~

= 0

.99

*Ta

rge

t +

0.6

8

Training: R=0.99392

Data

Fit

Y = T

20 40 60 8010

20

30

40

50

60

70

80

Target

Ou

tpu

t ~

= 0

.85

*Ta

rge

t +

4.7

Validation: R=0.99986

Data

Fit

Y = T

20 40 60 80

20

30

40

50

60

70

80

90

Target

Ou

tpu

t ~

= 0

.92

*Ta

rge

t +

2.9

Test: R=0.99822

Data

Fit

Y = T

20 40 60 8010

20

30

40

50

60

70

80

90

Target

Ou

tpu

t ~

= 0

.97

*Ta

rge

t +

1.5

All: R=0.99456

Data

Fit

Y = T

Fig. 5.5 Output vs. target regression plot

72

5.8 RSM and ANN predicted values Vs Experimental data

The RSM and ANN predicted data was compared with experimental values. It was

observed that the ANN prediction is almost similar to experimental values (Fig. 5.6)

Fig. 5.6 Comparison between the experimental data Vs RSM and ANN predicted data

5.9 SUMMARY

The production level was more enhanced through the optimization of physical

components such as, temperature, pH and incubation time by Response Surface

Methodology (RSM). The optimized medium components by RSM were considered

for the streamlining of these physical factors. Henceforth, the production was

enhanced from 63.54 U/ml to 91.32 U/ml, which were 6 folds higher in comparison

with initial activity.

73

CHAPTER-VI

CONCLUSIONS AND SCOPE FOR FUTURE WORK

6.1 Major findings from the thesis

The dairy effluent was screened for lactase producing bacterial strains. It was

observed that 46 bacterial isolates were able to produce lactase. The highest

yielding potential bacterial strain was named as VUVD001 and this strain was

identified as Bacillus subtilis through 16S rRNA sequence analysis.

The B. subtilis VUVD001 strain was also tested for an extracellular lactase

activity by native PAGE method with X-gal staining and found that the B.subtilis

VUVD001 produced an extracellular lactase enzyme.

The lactase activity was found to be 25.10 U/ml after optimization of fermentation

factors by OFAT.

The desired quantities of medium components were established by RSM for

enhanced production of lactase was lactose, 14.01 g/l; yeast extract, 10.30 g/l;

tryptophan, 0.43 g/l and MgSO4, 5.32 g/l. The optimized medium has produced

the lactase activity of 63.54 U/ml and it was almost three folds higher than the

unoptimized medium.

The effects of physical variables namely, temperature, pH and incubation period

on production were also studied by Box-Behnken Design. Under the optimized

conditions highest activity of lactase, 91.32 U/ml was obtained.

The bioprocess modeling study was carried out to relate the experimental data

with predicted observations by RSM. ANN model gave R2 value of 0.9945 for

RSM predicted data. Hence our findings reveal that B. subtilis VUVD001 was also

an alternative promising strain for commercial production of lactase.

6.2 Scope for future work

Lactase enzyme has commercial applications in dairy and pharmaceutical industries. It

has already been proved that many bacteria produced lactase enzyme to fulfill the

needs of lactose intolerants. The improved productivity of lactase enzyme has

achieved through the optimization of various process parameters by classical and

statistical approaches from newly isolated B.subtilis VUVD001 strain. However, there

is a lot of scope to enhance lactase production by strain improvement through genetic

modifications and also to increase the yield of lactase by scale-up of the process from

74

shake flask to reactor level. In addition, there is a further scope in the area of structural

characterization for complete elucidation of lactase from B. subtilis VUVD001.

75

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91

APPENDIX-A

(Chemical composition for lactase assay)

2.5mM ONP: To prepare 10 ml of 2.5mM ONP, 0.00347775 g of ONP was

dissolved into 10 ml of phosphate buffer.

1M Na2CO3: To prepare 1M Na2CO3, 10.6 grams of Na2CO3 was dissolved into

100ml of distilled water.

4mg/ml ONPG: To prepare 4mg/ml of ONPG, 0.020 grams of ONPG was

dissolved into 5 ml of phosphate buffer.

One liter of 0.1 M sodium phosphate buffer, pH 7 can be made by mixing the 57.7

ml 1M Na2HPO4 and 42.3 ml of 1M NaH2PO4 and the final volume make up to

one liter with distilled H2O.

Formula for caluculation of lactase activity

Enzyme activity (U/ml) = a x DF/ ε x T x enzyme suspension

Where ‗a‘ denotes the absorbance at 420 nm, ‗ε‘ represents the molar extinction

coefficient, ‗T‘ represents the incubation time and ‗DF‘ indicates dilution factor

92

APPENDIX-B

(Bacterial nutritional requirements)

Starch agar medium

Component g/l

Beef Extract 3

Soluble starch 10

Agar-Agar 12

MR-VP broth medium

Simmons citrate agar medium

Nitrate broth medium

Component g/l

Peptone 5

Dextrose or Glucose 5

di-potassium hydrogen

phosphate (K2HPO4) 5

Component g/l

Magnesium sulphate 0.2

Ammonium di hydrogen phosphate 1.0

Di potassium phosphate 1.0

Sodium citrate 2.0

Sodium chloride 5.0

Bromothymol blue 0.080

Agar-Agar 15

Component g/l

Peptone 5

Beef extract 3

Potassium nitrate 1

93

Phenol red broth medium

2 X Electrophoresis buffer or tank buffer

Chemical Final Concentration Amount

Tris base ( MW 121.1) 250 Mm 30.4 g

Glycine 1.92 M 144.0 g

Milli - Q water Dissolve & Make up To 1 lit

10% Resolving gel (10 ml)

Chemical Amount

Monomer 4 ml

1.5M tris pH 8.8 2.5 ml

Milli Qwater 3.3 ml

10% APS 0.1 ml

TEMED 0.004 ml

5% Stacking gel (5 ml)

Chemical Amount

Monomer 0.83 ml

1.5M tris pH 6.8 0.63 ml

Milli Qwater 3.4 ml

10% APS 0.005 ml

TEMED 0.005 ml

6X Sample loading buffer (100 ml)

Chemical Amount

Tris HCl 5.91 g

100% glycerol 48 ml

Bromophenol blue 30 mg

Component g/l

Peptone 10

Beef extract 1

NaCl 5

Phenol red indicator 0.018

94

APPENDIX-C

(Flow chart of the work)

95

LIST OF PUBLICATIONS FROM THESIS

T.C.Venkateswarulu, K. Vidya Prabhakar, R.Bharath Kumar, S.Krupanidhi,

Modeling and Optimization of Fermentation Variables for Enhanced Production of

Lactase by Isolated Bacillus subtilis strainVUVD001 using Artificial Neural

Networking and Response Surface Methodology, (2017), 3 BIOTECH (Accepted)

T.C.Venkateswarulu, K. Vidya Prabhakar, R.Bharath Kumar Optimization of

Nutritional Components of Medium by Response Surface Methodology for

Enhanced Production of Lactase, (2017), 3 BIOTECH (Accepted)

96

97

CURRICULUM VITAE

T.C.Venkateswarulu

Mail id: venki_biotech327@yahoo.com

Mobile: +91 9000390204

Current Status

Working as Assistant Professor in the Department of Biotechnology, Vignan‘s

Foundation for Science, Technology and Research University, Guntur, India.

Educational qualifications

Previous Experience

Worked as Asst.Professor in ALFA Engineering College, Allagadda, Department of

Biotechnology, from June- 2006 to July-2008.

Area of Research

Microbial Biotechnology and Bioprocess engineering

Qualification University/Institution Year of

Passing

%

Ph.D (Thesis

submitted )

VFSTR University, Guntur -- --

M.Tech.

Bio-Technology

ANU Campus,Guntur

2010

80%

B.Tech

Bio-Technology

Madanapalle Institute of

Technology and

Science, Madanapalle

.

2006 69%

98

Publications

SCIE Indexed

1. T.C.Venkateswarulu, K. Vidya Prabhakar, R.Bharath Kumar, S.Krupanidhi,

Modeling and Optimization of Fermentation Variables for Enhanced Production of

Lactase by Isolated Bacillus subtilis strainVUVD001 using Artificial Neural

Networking and Response Surface Methodology, (2017), 3 BIOTECH (Accepted)

2. T.C.Venkateswarulu, K. Vidya Prabhakar, R.Bharath Kumar Optimization of

Nutritional Components of Medium by Response Surface Methodology for

Enhanced Production of Lactase, (2017), 3 BIOTECH (Accepted)

3. R. R.Nair, N.T. Raveendran, V.R. Dirisala, M.B. Nandhini, T. Sethuraman, T.C.

Venkateswarulu, G. Doss, Mutational pressure drives evolution of synonymous

codon usage in genetically distinct Oenothera plastomes. Iranian Journal of

Biotechnology, (20014), 12(4), 58-72.

Scopus Indexed

1. Karlapudi, Abraham Peele, Indira Mikkili, T. C. Venkateswarulu, D. John Babu,

A. Ranganadha Reddy Bioconcrete Build Buildings with Quorum Sensing

Molecules of Biofilm Bacteria. Journal of Pharmaceutical sciences and Research,

(2016), 8, 10-12.

2. Y. Evangelin, T. C. Venkateswarulu, D. John Babu, K. Kasturi, Bacteriocins

from Lactic Acid Bacteria and Its Potential Applications. Int. J. Pharm. Sci. Rev.

Res, (2015), 32(1), 306-309.

3. M. Indira, T.C. Venkateswarulu, K. Chakravarthy, A. Ranganadha Reddy,

K.Vidya Prabhakar, Isolation and Characterization of Bacteriocin Producing

Lactic Acid Bacteria from Diary Effluent. Research J. Pharm. and Tech, (2015),

8(11), 1560-1565.

4. D. Sravani, K. Aarathi, N.S. Sampath Kumar, S. Krupanidhi, D. Vijaya Ramu,

T.C. Venkateswarlu, In Vitro Anti- Inflammatory Activity of Mangifera indica

and Manilkara zapota Leaf Extract. Research J. Pharm. and Tech, (2015), 10, 1-4.

5. A. Ranganadha Reddy, T.C.Venkateswarulu, M. Indira, A.V. Narayana,

Identification of Membrane Drug Targets by Subtractive Genomic Approach In

Mycoplasma Pneumonia. Research J. Pharm. and Tech, (2015), 8(9), 707 -714.

99

6. T.C.Venkateswarulu, B. Bodaiah, D. John Babu, A. Venkata Naraya, Y.

Evangelin, Bioethanol Production by yeast fermentation Using Pomace Waste,

Research J. Pharm. and Tech, (2015), 8(7), 841 – 844.

7. A.Ranganadha Reddy, T.C. Venkateswarulu, D. John Babu, M.Indira, Homology

Modeling Studies of Human Genome Receptor Using Modeller, Swiss-Model

Server and Esypred-3D Tools. Int. J. Pharm. Sci. Rev. Res, (2015), 30(1), 1-10.

8. T.C.Venkateswarulu, Kodali. V. Prabhakar, D. John Babu, R. Bharath Kumar,

A.Ranganadha Reddy, M. Indira, A. Venkatanarayan, Screening Studies on

Isozyme Pattern in all leaves of Dura Variety of Oil Palm (Elaeis guineensis Jacq.)

for Selection of Leaf Index. Research J. Pharm. and Tech, (2015), 8(1), 69 – 73.

9. TC Venkateswarulu, Kodali V Prabhakar, D John Babu, Rahul R Nair, A

Ranganadh Reddy, and A.Venkata Narayana. Studies on Electrophoretic Band

Pattern of Isozymes in all Leaves of Pisifera Variety of Oil Palm (Elaeis

guineensis Jacq). Research Journal of Pharmacy and Technology, (2014), 7(4),

415-418.

10. T.C. Venkateswarulu, C.V. Raviteja, Kodali.V. Prabhaker, D. JohnBabu, M.

Indira, A.RanganadhaReddy, A.Venkatanarayana, A Review on Methods of

Transesterification of Oils and Fats in Bio-diesel Formation. International Journal

of Chemtech Research, (2014), 6(4), 2568-2576.

11. T.C. Venkateswarulu,A. RanganadhaReddy, D. JohnBabu.Rahul.R.Nair, M.

Indira and A.Venkatanarayana, Comparative Studies of H5N1 Gene Segments

with other Subtypes of Influenza- A Virus Research Journal of Pharmaceutical,

Biological and Chemical Sciences, (2014), 5(3), 1417-1429.

12. K. Seetha Ram, T. C. Venkateswarulu, D. John Babu, Y. Sudheer, Seetharami

Reddy, J. B. Peravali and K. K. Pulicherla,‖ Cost Effective Media Optimization

for the Enhanced Production of Hyaluronic Acid Using a Mutant Strain

Streptococcus zooepidemicus 3523–7: A Statistical Approach‖ International

Journal of Advanced Science and Technology, (2013), 60, 83-96.

13. J.B. Peravali, S.R. Kotra, S. K. Suleyman, T.C. Venkateswarulu, K.V. Rajesh, K.

Sobha, K.K Pulicherla, Fermentative Production of Engineered Cationic

Antimicrobial Peptide from Economically Feasible Bacterial Host E. coli GJ1158.

International Journal of Bio-Science and Bio-Technology, (2013), 5(5), 211-222.

14. B. Sumalatha, A. Venkata Narayana, K. Kiran Kumar, D. John Babu and T. C.

Venkateswarulu, Design and simulation of a plant producing dimethyl ether

100

(DME) from methanol by using simulation software ASPEN PLUS, Journal of

Chemical and Pharmaceutical Research, (2015), 7(1), 897-901.

15. A. Ranganadha Reddy, T.C. Venkateswarulu, D. John Babu, N. Shyamala Devi

Homology Modeling, Simulation and Docking Studies of Tau-Protein Kinase.

Research Journal of Pharmacy and Technology, (2014), 7(3), 376-388.

16. Indira Mikkili, Abraham P Karlapudi, T.C.Venkateswarulu, D. John Babu, S.B.

Nath, Vidya P. Kodali, Isolation, Screening and Extraction of

Polyhydroxybutyrate (PHB) producing bacteria from Sewage sample.

International Journal of PharmTech Research, (2014), 6(2), 850-857.

17. B Sumalatha, Y Prasanna Kumar, K Kiran Kumar, D John Babu, A Venkata

Narayana, Maria Das, and TC Venkateswarulu, Removal of Indigo Carmine

from Aqueous Solution by Using Activated Carbon. RJPBCS, (2014), 5(2), 12-22.

18. D. John Babu, B. Sumalatha, T.C. Venkateswrulu, K. Mariya Das and Vidya P.

Kodali, Kinetic, Equilibrium and Thermodynamic Studies of Biosorption of

Chromium (VI) from Aqueous Solutions using Azolla filiculoidus. JPAM, (2014),

1, 3107 – 3116.

19. K. Murali Krishna, T.C. venkateswarulu, K. Ravikanth reddy, Venkat R. konda,

G. T. Rajesh T. srivalli, Bioprocess optimization and characterization

ofrecombinant urate oxidase expressed in Escherichia coli. International Journal

of Pharma and Bio Sciences, (2013), 4(4), 608 – 616.

20. Vidya P. Kodali., Karlapudi P. Abraham., J. Srinivas, T.C.Venkateswarulu, M.

Indira, D. John Babu, T. Diwakar, K.L. Vineeth, Biosynthesis and potential

applications of bacteriocins. Journal of Pure and Applied Microbiology, (2013), 4,

2933-2945.

21. J. B. Peravali, K. Seetha Ram, S. K. Suleyman, T. C. Venkateswarlu, KV Rajesh,

K. Sobha and K. K. Pulicherla, Fermentative Production of Engineered Cationic

Antimicrobial Peptide from Economically Feasible Bacterial Host E. coli GJ1158.

International Journal of Bio-Science and Bio-Technology, (2013), 5(5), 211-222.

22. Karlapudi P. Abraham, J. Sriniva, T.C. Venkateswarulu, M. Indira, D. John

Babu, T. Diwakar, Vidya P. Kodali, Investigation of the Potential Antibiofilm

Activities of Plant Extracts. International Journal of Pharmacy and Pharmaceutical

Sciences, (2012), 4, 282-285.

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23. Devika.P, R.OletiSreedara, T.C.Venkateswarulu, N.D.Prasanna Computational

understanding of selective MMP inhibitors in COPD. International journal of

pharmaceutical science and research, (2012), 3(12), 4959-4975.

National Conferences

1. Presented a paper entitled Comparitive modeling, simulation and docking studies

of TAU-Protien kinase in National Seminar entitled New Horizons In

Biotechnology organized by KVR Womens Degree college Kurnool sponsored by

UGC NewDelhi on 9th

Feb to 10th

Feb, 2015.

2. Presented a paper entitled Isolation, screening and Characterization of Lactase

producing bacteria from Dairy Effluent in Indian Youth Science Congress

organized by MSSRF, SRM & ANU from 19th

Jan to 21st Jan, 2015.

3. Presented Molecular cloning, Expression, Purification and Characterization of

recombinant urate oxidase at A.P. Science Congress-2012 from14th

Nov to 16th

Nov 2012, Jointly Organized by Andhra Pradesh Academi Sciences, Hyderabad

and Dept. of Biotechnology, Achyarya Nagarjuna University, Guntur-522510 A.P.

India.

4. Presented a paper on Isiolation, Screening and Purification of Laccases From a

Novel Fungal Species in national conference on Technological Advancements In

Biotechnology organized by Department of Biotechnology JNTU, Pulivendula on

7th

Aug to 8th

Aug, 2012.

International Conferences

1. Presented a paper on Cellulase Production through Submerged Fermentation from

Newly Isolated Bacterium from Sewage Waste at global summit on Emerging

Science and Technology: Impact on Envirnoment and Human Health Jointly

organized by VS University, Nellore, India and UNT Health Science Center,

Forthworth, Texas, USA.

2. Presented a paper on Studies on Electrophoresis pattern of Isozymes and Proteins

in Different Leaves of Dura variety of oil palm (Elaeis guineensis jacq) presented

at ―International Conference on Chemical and Bioprocess Engineering ―

Organized by Schools of Chemical Engineering of NIT Warangal, Warangal on

16th &17th, November, 2013.

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3. Presented a paper on Biosorption of Cromium from Industrial Waste Water Using

Azolla Filicuoidus organized by Department of Biotechnology, VFSTR

University, Vadlamudi on 23rd

Aug to 24th

Aug, 2013.

4. Presented a paper on Expression and Purification of recombinant therapeutic

protein (PDGF-AA) international conference on Biotechnology held at Hyderabad

on 02th

May to 05th

May, 2012 conducted by OMICS Publishing group.

5. Presented a paper on Extracellular Biosynthesis of Silver nano particles by using

Bacterial Species in the International symposium on Role of Nanotechnology&

Biomedical in Health Care at ANU Campus, Guntur on 19th

Dec to 21th

Dec, 2009.

Training Programs and Workshops attended

1. Attended the five days workshop held at Deparment of Biotechnology, NIT,

Warangal on 2nd

to 6th

Nov, 2015 on Modeling, Simulation and Optimization of

Bioprocesses.

2. Attented the three day work shop from 26th

Nov to 28th

Nov, 2015 on Biosignal

Processing and Measurments and Computing Everywhere organized by

Department of Electronics and Communication Engineering, VFSTR University,

Vadlamudi.

3. Attended a three day Indo-US work shop from 26th

Sep to 28th

Sep, 2014 on

Bacterial Antibiotic Resistance and Nanotechnologies organized by Department of

Biotechnology, VS University, Nellore.

4. Attended a three day INDO-US Workshop on Bacterial Antibiotic Resistance and

Nanotechnologies held during 26th

Sep to 28th

Sep, 2014, at Department of

Biotechnology, Vikrama Simhapuri University, Nellore.

5. Attended the workshop held at Dept. of Biotechnology, NIT, Warangal on 8th

Nov

to 10th

Nov, 2013 on Modeling, Simulation and Optimization of Bioprocesses.

6. Attended the workshop held at Department of Biotechnology, Indian Institute of

Technology, Madras on 5th

Jul to 9th

Jul, 2011 on Summer Workshop on

Bioreactors.

7. Attended the workshop held at Sri Venkateswara College of Engineering,

Sriperumbudur on 27th

Sep to 28th

Sep, 2010 on Recent Trends in Instrumentation

for Quality Control and Quality Assurance in Bioprocess Industries.

8. Attended 10 days training held at VFSTR University, Vadlamudi from 26th

July to

5th

Aug, 2010 on Faclty Induction Program.

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9. Attended the Indo-US 5 days work shop from 5th

July to 9th

July, 2010 at VFSTR

University, Vadlamudi on Chemical and Biochemical Process Control.

Membership in Professional bodies:

Life member of the International Association of Engineers (IAENG) with

membership number 136737

Life member of Asia-Pacific Chemical, Biological& Environmental Engineering

Society (APCBEES) membership number 201729

Life member of Society for Biotechnologists(India) with membership number 770

Personal Profile

Name : T.C.Venkateswarulu

Date of Birth : 16-06-1985

Father‘s Name : T.C.Subbarayudu.

Address Erraguduru (P)

Pamulapadu (M)

Kurnool (D)

Andhra Pradesh, PIN-518442

Languages Known : English and Telugu

Declaration

I hereby confirm that the above information given here are true to the best of my

knowledge and belief.

T.C.Venkateswarulu